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Decoding Riparian Living in territories of Instability

Christian Gutierrez (M.Arch) Samarpita Sinharay (M.Arch) Xinyu Zhang (M.Sc) Zhongyu Zhang (M.Sc)

Decoding Riparian Living in territories of Instability

Christian Gutierrez (M.Arch) Samarpita Sinharay (M.Arch) Xinyu Zhang (M.Sc) Zhongyu Zhang (M.Sc)

ARCHITECTURAL ASSOCIATION SCHOOL OF ARCHITECTURE GRADUATE SCHOOL PROGRAMME

PROGRAMME:

Emergent Technologies and Design [EmTech]

TERM:

2025 - 2026

COURSE TITLE:

M.Sc, Dissertation

COURSE TUTORS:

Founding Director: Dr. Michael Weinstock Course Director: Dr. Milad Showkatbakhsh Studio Master: Dr. Anna Font Studio Tutors: Abhinav Chaudhary, Paris Nikitidis, Krishna Bhat, Danae Polyviou, Dr. Alvaro Velasco Perez

SUBMISSION DATE:

18 September, 2026

STUDENT NAME(S):

Christian Gutierrez, Samarpita Sinharay, Xinyu Zhang, Zhongyu Zhang

DECLARATION:

“I certify that this piece of work is entirely my/our own and that any quotation or paraphrase from the published or unpublished work of others is duly acknowledged.”

SIGNATURE OF STUDENTS:

Christian Gutierrez

DATE:

18 September, 2026

Samarpita Sinharay

Xinyu Zhang

Zhongyu Zhang

ABSTRACT This proposal investigates how settlement can coexist with landslide-prone landscapes by working with the processes that shape them rather than attempting to resist these processes permanently. Instead of positioning architecture as a direct response to instability, the research develops an adaptive settlement framework for environments where water, ground, and occupation remain in continuous transformation. Landslides are understood as part of a dynamic territorial condition, raising the question of how hydrological intelligence can guide where, under what conditions, and with what degree of permanence settlement can occur. Escazú, Costa Rica, serves as the testbed. Its steep terrain, intense rainfall, and expanding urbanisation create conditions in which altered drainage can increase soil saturation and landslide susceptibility. The territory is read through ecotonal gradients, distinguishing areas that require protection, progressive stabilisation, or occupation. Computational hydrological simulations trace water movement through the watershed and inform a branching network of channels, terraces and collection landscapes that follows the existing topography rather than overriding it. Material research extends this hydrological logic into the construction system. Rice-husk ash, lime and locally available aggregates are developed into materials with differentiated water and mechanical behaviours: hydrophobic mixtures form drainage channels that convey water, while hydrophilic mixtures define collection nodes that retain and gradually absorb it. This hydrological gradient is combined with bioremediation and planted terraces, which progressively stabilise vulnerable ground, reduce erosion, and restore degraded areas. Agricultural terraces extend this process into productive landscapes, linking ecological recovery with local cultivation and supporting an agro-urban settlement strategy. Architecture responds to these changing landscape conditions through varying degrees of permanence. Adaptable bamboo structures occupy areas where environmental change remains possible, while permanent buildings develop along more stable contours and terraces. Through the integration of computational hydrology, bioremediation, material experimentation, and architectural design, the project proposes a transferable framework for determining how settlement can adapt to the changing conditions of a dynamic landscape. Resilience is therefore understood not as permanently fixing unstable ground, but as allowing settlement to evolve according to the changing capacity of the landscape to support it. Keywords: landslide risk, hydrological branching, soft boundaries, agro-urbanism, soil stabilisation, adaptive settlement

CONTENT · ABSTRACT

· INTRODUCTION

· DOMAIN · Landscape Dynamics · Landslide Factors · Case Studies - Calle Lajas · Site Study · Environmental Study · Climate Analysis · Conclusion · Case Study - Community Development - Soil Stabilization - Bioremediation Interventions · Research Questions · Hypothesis · Conclusion · Chapter Bibliography

· METHODOLOGY · Gradient Mapping · Height-Field Model · Branching Algorism · Structural Analysis - Karamba · Computational Fluid Dynamics (CFD) · Machine Learning · FEA Optimisation - Ameba · Optimisation - Graphic Statics · Material Hydrology · Conclusion · Chapter Bibliography

· RESEARCH DEVELOPMENT · Phase I Development · Site Selection · Pre-intervention Simulation · Channel Performance Experiment · Channel Network · Conclusion · Chapter Bibliography

22 23 24 26 34 36 46

48 48 52 54 56 58 60 61

68 69 70 73 74 75 76 77 78 80 81

82 86 88 92 94 98 106 107

· MATERIAL EXPERIMENTATION · Introduction - Research Aim - Hydrophobic and Hydrophilic Strategy - Material Selection (RHA) and Development Frame work · Preliminary Physical Experiment - Experimental Materials - Material Experiment Process - Stage 1 - Hydrophobic Test - Stage 2 - Hydrophilic Test - Stage 3 - Inclusion of Fibers · Selected Samples for Further Research · Comparative Analysis · Conclusion · Chapter Bibliography

107

· DESIGN DEVELOPMENT

134

· Water Channel Materialisation - Water Channel and Hydrological Distribution - Channel Construction Logic - Tessellation Aggregation - Tessellation Details - Finite Element Analysis (FEA) - Comparative Analysis - Fabrication of Tessellated Components · Phase II Development - Three Intervention Zones - Topological Network and Program Mix - Functional Planning and Spatial Allocation

112 112 113 114 116 116 118 120 121 122 124 129 131 132

138 138 140 142 144 145 146 147 148 150 154 155

- Cellular Automata(CA) Distribution of Different Bioremediation Rules - Bioremediation and Forestry - Agro - Production Distribution - Terrace Variations - Channel Variations · Spatial Gradients for Functions · Conclusion · DESIGN PROPOSAL · Phase III Development · Architectural Development - Master Plan - Function Transitions - Temporary to Permanent Structures - Form Finding · DISCUSSION · Hydrological & Territorial Framework · Computational Experiments · Material Experiments

157 158 160 162 164 166 168

170 174 176 176 178 180 182

192 194 194 195

· Architectural & Social Ecological Implications · Conclusion · APPENDIX · Historical Precipitaton Record · Parametric Environmental Infuence-Field · Channel Network Development · Cellular Automata (CA) · Agro-Productve Landscape Strategy · Differential Growth Field · Terrace Formaton · Functonal Clustering

195 196

202 204 206 210 222 232 236 242 246

INTRODUCTION Landslides are commonly described as natural disasters, yet the movement of soil, rock, water, and vegetation is intrinsic to the way landscapes evolve. Disaster occurs when these shifting conditions intersect with settlements and infrastructure designed on the assumption of stable ground. In rapidly urbanising mountain regions this conflict intensifies as development interrupts drainage, removes vegetation, and places additional load on already vulnerable slopes. The 2010 Calle Lajas landslide in San Antonio de Escazú, Costa Rica, renders this conflict visible. Heavy rainfall associated with Tropical Storm Tomas triggered the failure, but its scale was determined by a longer interaction between unstable terrain, concentrated water flows, and exposed patterns of occupation. Calle Lajas therefore reveals more than the consequences of an extreme storm; it exposes the inability of conventional settlement systems to respond to the landscape’s dynamic behaviour. Prevailing responses to landslide risk depend on exclusion zones, retaining structures, and centralised drainage. While such measures may offer local protection, they treat instability as a fixed condition with a clearly defined edge. Decoding Riparians instead conceptualises risk as a shifting territorial gradient produced by the movement of water, the condition of soils, the growth of vegetation, and patterns of land use. The research asks how an adaptive settlement framework might transform landslide-prone territories from zones of exclusion into landscapes of managed coexistence. It proposes a phased system in which hydrological branching guides water through the terrain, rice husk ash–based composites enable targeted soil stabilisation, and ecological restoration prepares selected areas for productive and habitable use. Architecture is thereby reframed not as a static object set against instability, but as an evolving territorial strategy that works with the landscape over time.

17 16

Domain chapter

DOMAIN

Domain chapter

DOMAIN Globally, landslides are disasters triggered by natural forces1, but critically amplified by human intervention. This chapter frames the relationship between unstable ground and the manner in which it is occupied. Rather than treating landslides as isolated natural events, it examines how slope movement becomes disastrous when hydrological and geological processes are intensified by settlement, infrastructure, and land-use change2. The Calle Lajas landslide in Escazu, Costa Rica, provides the central case through which this conflict is explored. When read across multiple scales, it demonstrates how risk is produced through the interaction of environmental susceptibility and human intervention. By analysing data on slope remediation and urban development models, this contests the assumption that landslide risk can be contained by a single boundary between safe and unsafe land. Instead it reveals a gradient of unstable factors that shifts across the territory. To develop a framework distinguishing where landscape remains open and where to repair, a critical gap helps harmonise both sides of the ecological system.

1 Highland, L.M. and Bobrowsky, P. (2008) The Landslide Handbook — A Guide to Understanding Landslides, Circular 1325. Reston, VA: U.S. Geological Survey. 2 Froude, M.J. and Petley, D.N. (2018) 'Global fatal landslide occurrence from 2004 to 2016', Natural Hazards and Earth System Sciences, 18(8), pp. 2161–2181. doi: 10.5194/nhess-18-2161-2018.

Domain chapter

Landscape Dynamics

Landslides occur under widely differing environmental and social conditions, yet their consequenc- Figure 1.1 Global landslide instances es are invariably shaped by the relationship between moving ground and human occupation1. A comparative review of selected global events reveal that these disasters are driven by varying degrees of natural and human factor2. It establishes this broader context, demonstrating how similar physical processes produce different outcomes depending on the local context. What it leads to is an unpredictable recurring event in the world.

A recent example is the massive August 2026 glacier-induced lanslide in Nepal3. Within this global field, Costa Rica is selected as the principal territory of investigation. Its mountainous relief, active geology, and intense seasonal rainfall generate recurring conditions of instability4, particularly where urban growth extends onto vulnerable slopes, and into drainage networks5. The study then narrows to the 2010 failure in Escazú, to investigate the landslide prone territory, as a base for our research.

3 U.S. Geological Survey (2026) 2026 Nepal Debris Avalanche and Flash Flood. Landslide Hazards Program. 4 Quesada-Román, A. (2024) 'Mass movements dynamics and morphologies in Costa Rica', in Quesada-Román, A. (ed.) Landscapes and Landforms of Costa Rica. Cham: Springer (World Geomorphological Landscapes), pp. 79–94. doi: 10.1007/978-3-031-649400_4. 5 Quesada-Román, A. (2021) 'Landslide risk index map at the municipal scale for Costa Rica', International Journal of Disaster Risk Reduction, 56, article 102144. doi: 10.1016/j.ijdrr.2021.102144.

Landslide Factors

Seismic vibrations

Rotational

Anthropogenic factors

Water overflow from crater lakes Undercutting of slopes by streams or rivers

Translational

Groundwater changes

Differential weathering Debris and deadwood slide

Physical or chemical weathering

Block slide

Altering drainage patterns

Instense or sustained rainfall Intense water flow from heavy rain

Debris Avalanche

Topple

Slope failure does not follow a single mode of movement. As illustrated in Figure 1.4, it may oc- Figure 1.2 Natural triggers of landslide cur as slides, flows, falls, or slow deformation, depending on the composition of the ground, the instability across a hillslope geometry of the slope, and the manner in which water moves through it6. Such events are rarely Figure 1.3 Types of landslides attributable to one isolated cause; they emerge from pressures that accumulate over time through weathering, seismic disturbance, stream erosion, and the progressive weakening of soil and rock7.

Rockfall

Earthflow

Figure 1.4 Anthropogenic factors of landslide instability across a hillslope

Creep

Deforestation Oversteepening slopes

Introducing water via irrigation, reservoirs, or leaking pipes

Lateral Spread

Domain chapter

Natural factors

Urban construction Overloading the tops of slopes

Debris Flow

Development can convert latent instability into active slope failure by disrupting the mechanical and hydrological balance of the terrain9. As shown in Figure 1.3, vegetation clearance removes root reinforcement and exposes the ground to erosion10, excavation and over-steepening alter the natural geometry of the slope; and construction adds load to vulnerable ground, particularly near edges and cut faces11.

Rainfall is especially significant because it links surface and subsurface processes. As water infiltrates the ground, saturation and pore-water pressure rise, reducing the slope’s capacity to resist movement8, while runoff simultaneously erodes exposed surfaces and concentrates flow along vulnerable paths. Figure 1.2 therefore presents slope failure not as the product of a single trigger, but as the visible threshold of a protracted geological and hydrological process.

Water management compounds these disturbances. Where development interrupts natural drainage, runoff is redirected from diffuse pathways into fewer, more concentrated routes, allowing moisture to accumulate in already weakened ground. Such interventions do not merely increase exposure to an existing hazard; they alter the very processes that produce it — accelerating saturation, erosion, and material weakening12, and bringing the slope progressively closer to failure.

6 Hungr, O., Evans, S.G., Bovis, M.J. and Hutchinson, J.N. (2001) 'A review of the classification of landslides of the flow type', Environmental & Engineering Geoscience, 7(3), pp. 221–238. doi: 10.2113/gseegeosci.7.3.221. 7 Fan, X., Xu, Q., Scaringi, G., Li, S. and Peng, D. (2017) 'A chemo-mechanical insight into the failure mechanism of frequently occurred landslides in the Loess Plateau, Gansu Province, China', Engineering Geology, 228, pp. 337–345. doi: 10.1016/j.enggeo.2017.09.003. 8 Hu, W., Scaringi, G., Xu, Q., Van Asch, T.W.J., Huang, R. and Han, W. (2018) 'Suction and rate-dependent behaviour of a shear-zone soil from a landslide in a gently-inclined mudstone-sandstone sequence in the Sichuan basin, China', Engineering Geology, 237, pp. 1–11. doi: 10.1016/j.enggeo.2018.02.005.

9 Laimer, H.J. (2017) 'Anthropogenically induced landslides — A challenge for railway infrastructure in mountainous regions', Engineering Geology, 222, pp. 92–101. doi: 10.1016/j.enggeo.2017.03.015. 10 Stokes, A., Norris, J.E., van Beek, L.P.H., Bogaard, T.A., Cammeraat, L.H., Mickovski, S.B., Jenner, A., Di Iorio, A. and Fourcaud, T. (2008) 'How vegetation reinforces soil on slopes', in Norris, J.E. et al. (eds.) Slope Stability and Erosion Control: Ecotechnological Solutions. Dordrecht: Springer, pp. 65–118. doi: 10.1007/978-1-4020-6676-4_4. 11 Quesada-Román, A. (2022) 'Disaster risk assessment of informal settlements in the Global South', Sustainability, 14(16), article 10261. doi: 10.3390/su141610261. 12 Tarolli, P. and Straffelini, E. (2020) 'Agriculture in hilly and mountainous landscapes: Threats, monitoring and sustainable management', Geography and Sustainability, 1(1), pp. 70–76. doi: 10.1016/j.geosus.2020.03.003.

Domain chapter

Case Studies - Calle Lajas

Calle Lajas offers a critical demonstration of how an environmental trigger becomes a disaster Figure 1.5 Calle Lajas case study: natural when it acts upon a landscape already altered by occupation. Rainfall associated with Tropical and anthropogenic triggers of landslide Storm Tomas rapidly saturated the steep, deeply weathered slope, raising pore-water pressures beyond the terrain’s capacity to remain stable13. The severity of the failure, however, cannot be explained by rainfall alone. Settlement had progressively extended into natural drainage and runout areas14, while fragmented development disturbed the movement of water across and beneath the slope, concentrating moisture in vulnerable ground and diminishing the landscape’s capacity to absorb, slow and safely redirect runoff. Figure 1.5 therefore presents the event as the outcome of an accumulated territorial condition rather than a single climatic episode.

13 Centeno-Morales, J., Vargas-Trejos, Y., González-Varela, M. and Alfaro-Orozco, E. (2020) 'Estudio de caso: Lajas Compartir, un acercamiento al desplazamiento ambiental en Costa Rica', Revista Geográfica de América Central, 64(1), pp. 179–201. doi: 10.15359/ rgac.64-1.7. 14 Arias, M., Fuentes, O. and Fallas, J. (2011) 'Criterios utilizados para la microzonificación de la microcuenca Lajas, Cantón de Escazú, Costa Rica', En Torno a la Prevención, 7, pp. 1–7.

Figure 1.6 Photograph of the Calle Lajas landslide (Source: Jorge Castillo, Isaac Villalta, Randal Sandoval and Archive)

Site Study Costa Rica

San José

N N

ESCAZU

Domain chapter

San Jose partical map

Legend

The site analysis begins by situating Escazú within Costa Rica’s broader geological instability. Figure 1.7 shows that landslide susceptibility is not confined to isolated locations but constitutes a wider territorial condition shaped by the country’s active ground and recorded history of slope failure15, with San José providing the urban reference from which the study approaches the more specific conditions of Escazú. This change of scale connects regional instability with its local expression. Rather than reading the site as a stack of discrete environmental layers, the analysis considers how ground, water and patterns of occupation operate together — establishing why certain areas of Escazú are more vulnerable and how future interventions should respond.

15 Quesada-Román, A. (2021) 'Landslide risk index map at the municipal scale for Costa Rica', International Journal of Disaster Risk Reduction, 56, article 102144. doi: 10.1016/j.ijdrr.2021.102144.

Figure 1.7 Geological hazards and landslide context of Costa Rica

Figure 1.8 Location of San José

Escazú lies where the metropolitan expansion of San José meets the rising terrain of the Cerros de Escazú. As shown in Figure 1.8, this is not a simple urban edge but a shifting interface in which settlement presses into a landscape structured by steep slopes, moving water and ecological continuity. This position makes the municipality a critical territory for the research: it demonstrates how urban growth intensifies existing instability when development advances without responding to the behaviour of the ground15. The metropolitan–mountain interface is accordingly treated as a spatial condition to be negotiated rather than a fixed boundary between city and landscape.

Escazú

Geosystem: ecological fragility, human occupation, geological processes, and cultural history

Domain chapter

Hydrological role: Several tributary rivers originate here – watersheds support the drinking water supply for the southern part of San Jose.

Escazu is defined by an established boundary zone (in 1970) creating a protected area safeguarding Figure 1.9 Mountains of Escazú (top) the forest , upper slopes and critical ecological corridors16. The region’s spatial continuity is characterised by a transition from the diverse vegetation and topography reserved in the mountains near Figure 1.10 Houses in the mountains of Pico Blanco down to the dense sector urbanised in the valleys. Escazú (bottom) This gradient creates a complex ecological mix where forest and urban intertwine near the boundary. Descending water tributaries merge into the residential urban framework, and government regulated hard boundaries are set to manage growing tension between advancing city settlements and the highly sensitive natural landscape. 16 Costa Rica (1976) Decreto Ejecutivo No. 6112-A: Creación de la Zona Protectora Cerros de Escazú, 17 July. San José: La Gaceta.

Figure 1.11 Spatial overview of Escazu with the boundary protected zone

Flora and Fauna

Land-use Agriculture

purple pigeons

Main data

Total farms

53 farms

Total farm area

57.6 ha

Average farm size

≈ 1.09 ha/farm

Main farm activity

47 agricultural farms and 6 livestock/pecuary farms

Area by activity

Agricultural: 38.5 ha; Pecuary: 19.2 ha

Land use type

Area

Share of total farm area

Arable land

24.4 ha

42.4%

Permanent crops

11.4 ha

19.8%

Pastures

9.4 ha

16.3%

Tree Forests

7.6 ha

13.2%

Other uses

4.8 ha

8.3%

coatis

Domain chapter

Native trees 8 - 25 m

Bamboo-type vegetation 2 - 8 m

Domain chapter

Oak forest 15 - 30 m

Type of crops: Parsley, Onion, Tomato, Coffee, Grass(Cattle), Carrot Fruit Trees: Orange, Mango, Avocado, Banana, Plantain Basic Grains: Rice, Maize, Beans Roots & Tubers: Cassava, Potato Cucurbits: Watermelon & Melon

Shrubs and woody plants 1-8m

Ferns and herbs 0.2 - 1.5 m weasels

squirrels

rabbits

coyotes

The Cerros de Escazu functions are a highly diverse and connected ecological system where lay- Figure 1.12 Flora and fauna analysis of ered forest systems actively shape the territorial resilience17. The rich vegetation hosts a variety of Escazú plant spcies that thrive by deeply anchoring into the terrain, enabling them to survive and stabilise shifting environmental conditions. This intercepts exposed soil, fragile to saturation due to heavy rainfall and regulated subsurface water movements18. The biodiversity provides a resilient biological framework capable of sustaining broad habitats and driving the gradual recovery of disturbed ground. With human migration into the ecological reserve, deforestation and heavy construction threatens this natural balance, making the landscape more succeptible to rupture.

Category

17 Sistema Nacional de Áreas de Conservación (SINAC) (2015) Plan General de Manejo de la Zona Protectora Cerros de Escazú 2015–2025. San José: SINAC, Ministerio de Ambiente y Energía. 18 Lann, T., Bao, H., Lan, H., Zheng, H., Yan, C. and Peng, J. (2024) 'Hydro-mechanical effects of vegetation on slope stability: A review', Science of the Total Environment, 926, article 171691. doi: 10.1016/j.scitotenv.2024.171691.

Figure 1.13 Fragmented agricultural fields along the urban–mountain Interface of Escazú

Drawing from the same immence soil fertility that sustains the region’s wild ecosystem, Escazu has evolved into a thriving agricultural hub supporting diverse crop plantations19. Cultivations directly support the region’s environmental goals by simultaneously stabilising local livelihood, managing water runoff, and establishing a viral strategy for responsible territorial stabilisation amidst mounting urban demands. The continued presence of small-scale farming gives this transition a productive role within the project. Rather than treating agriculture as a discrete land use, the research examines how cultivation can be used to stabilse the natural forces and sustain livelihood. Figure 1.13 compares the existing agricultural infrastructure that can be used in the project. 19 Piedra González, M.A. (2006) Estudio social en la zona agrícola del cantón de Escazú. Informe del proceso de formulación del Plan Regulador para la Zona Agrícola del Cantón de Escazú. Escazú: Municipalidad de Escazú.

Landuse in Escazú

Ecological Corridor

N N

1970

Pro

tec

ted

1970

2009

1970

2009

Zo n

2014

Domain chapter

1970

2014

2025

2014

2025

2014

Government response to fixing ecological balance, is creating a zonal regulation20. Consolidating city in the north and allowing forested mountain in the south. The shift is not set abruptly but allowed to transition through an agriculture zone shown in Figure 1.15. This intermediate zone was done to create a buffer between urban growth and natural reserve, with the agriculture being an active operator. A distributed land-use, containing different functions is expected to negotiate between urban occupation, agriculture practices and ecological protection. The effect- overused zones with heavy construction, and agriculture pockets of convinience. 20 Municipalidad de Escazú (2025) Plan Regulador del Cantón de Escazú. Escazú: Municipalidad de Escazú.

Figure 1.14 Land use, hydrology and ecological corridors in Escazú. (Source: QGIS data) (left) Figure 1.15 Agriculture and overused zones (right)

Figure 1.16 Transformation of Escazú’s The comparison between 1970 and 2025 reveals the continuous expansion of settlement into proecological corridor under urban Expansion, tected and agricultural boundaries21. This urban encroachment fragments the corridor and weakens 1970–2025 its ecological continuity. Growing population and urban demands, led people to accept the dangers

of nature and travel into the protected region. A growing population inside the boundary protected zone, as shown in Figure 1.16, exposes the fragility of of municipality protocols and regulations. Thus this study contests the idea of rigid boundaries that eventually fail when faced with urban demands.

21 Sistema Nacional de Áreas de Conservación (SINAC) (2015) Plan General de Manejo de la Zona Protectora Cerros de Escazú 2015–2025. San José: SINAC, Ministerio de Ambiente y Energía.

Environmental Study Terrain map

Steep Slopes Terrain above 25 degrees are susceptible to erosion

Domain chapter

Central Plateau Low steep, flat area

The terrain changes markedly from the more open urbanised areas in the north to the compressed Figure 1.17 3D Terrain map of Escazu (left) contours of the southern mountains, defined by steepness and direction of drainage. Contour mapping clarifiesthis landscape structure by distinguishing where steep slope accelerate runoff, and concentrate sediment, while gentle areas where conditions are sufficiently controlled for the Figure 1.18 Slope direction (top) and ratio (bottom) of Escazú urban framework.

Figure 1.19 Terrain and Contour Analysis of Escazú

Understanding this dynamic relationship between slope and aspect provides the base for locating strategic interventions and organising developement around the inherent behaviour of nature.

Climate Analysis Precipitation

Domain chapter

Precipitation is distributed around Escazu driven by a complex combination of terrain orientation and altitude. Higher, elevated regions recieve more rainfall compared to the lower plateau areas. October marks the regions’s most intense precipitation season, while February the regions face a draught22. Notably the annual rainfall recording a devasting maximum of 466mm during the Landslide occurance in Calle Lajas. A predictive model indicates an increase of 13.4% rainfall annually in any of the coming 50 years. Because the slopes of different degrees retain and release moisture at vastly different rates, terrirtorial instability cannot be assessed through this gradient alone. This climatic data combined with topological reading directly will distinguish highly vulnerable zones suited for stabilisation stategies

22 Meteoblue (2026) Climate and weather history: Escazú, Costa Rica. Available at: https://www.meteoblue.com (Accessed: 10 May 2026).

Figure 1.20 Monthly precipitation table

Figure 1.21 Annual peak precipitation documentation and prediction

Figure 1.22 Precipitation distribution in October- highest precipitation (Source: meteoweather)

Wind Influence

Domain chapter

Predominant winds arriving from the northeast are strongly conditioned by Escazu’s varying al- Figure 1.23 Predominant with regional wind titude and topography22. Rather than moving uniformly they are redirected as it passes complex flow direction terrain variation, producing highly localised and variable wind exposure. Wind is treated as a supporting environmental parameter rather than a direct indicator of slope failure. Although strong seasonal winds impose additional loads on exposed terrain, their principal relevance lies in their influence on evaporation, vegetation growth, and the comfort of future outdoor or lightly built spaces. Read alongside terrain and moisture conditions, these patterns help refine where ecological restoration, cultivation, and later occupation can be most effectively located.

Figure 1.24 Wind flow Map of Escazú

Soil Mapping

Landslide soil bodies Areas containing loose soil that may erode to cause landslides Landslide possible zones Steep slopes with high risk of landslide and rockfall Unstable slopes Unstable active landslide, needing immediate action

Domain chapter

Active impact line High impact fault lines that may trigger landslides Avalanche prone areas On the periphery of the water bodies, prone to erosion

③ ②

①

Soil Type

Soil mapping provides a critical analytical layer for understanding the material conditions that con- Figure 1.25 Soil instability typologies and tribute to landslide susceptibility in Escazú. The distribution of specific types, such as the Utisols landslide risk and Inceptisols, is particularly significant because these sentive soil bodies rapidly lose cohesion under prolonged saturation23. The geological composition reveal a patchwork formation, showing areas of weathered foundation, indicating the fragile framework different areas. Rather than indicating risk through surface gradient alone, the mapping reveals how vulnerable soil bodies, geological weakness, and hydrological exposure combine to shape areas of instability. By locating these sensitive soil zones, the analysis provides a spatial basis for positioning stabilisation measures, drainage control, and future design interventions.

23 Soil Survey Staff (2014) Keys to Soil Taxonomy. 12th edn. Washington, DC: United States Department of Agriculture, Natural Resources Conservation Service.

Urbano Utisols/ Inceptisols Utisols

Figure 1.26 Soil type and geological distribution of Escazú

Soil Stratification and Geological Section

200m

Inceptisols Well-drained acids: Common in steep areas, they represent a large part of this order. Fertile/Well-drained: With high agricultural potential, representing 22% of these soils. Poorly drained (Aquepts): Located in low-lying areas.

Urbano urban land is not classified by its physical soil type (sand, clay), but by its use and development potential . The main term used is... urbanized or developable land

Geological composition

Section 1-1

2000 m Domain chapter

Domain chapter

Utisoles High Acidity: Low base saturation, which limits the availability of nutrients for some crops. Structure: They are characterized by the accumulation of clay in the subsoil (B horizon). Formation: They develop in undulating to steep reliefs (slopes), often on sedimentary materials. Use: Despite their low natural fertility, they can be used with management techniques (plowing), although in many areas they are found under pastures or forest

Soil layer

LEGEND Slump deposits Sandy loam (mostly alluvium) Loess (mostly reworked) Periglacial deformations Clay loam with gleyzation Akchagyl clays

Section 2-2

1500 m

Subsurface soil sections further expose the geological vulnerabilities driving the instability, reveal- Figure 1.27 Soil stratification and geological ing a heterogenous upper soil layer reaching up to 200m in depth24. Here water easily infiltrates section of Escazú with key map and accumulates. The underlying geological structures form varying formations, with its instability frequently concentrating in areas of weathered beds and where contrasting material meet25. These sections identify highly vulnerable slump deposits, clay loams and sandy loams at the base slopes, each possessing drastically different capacities for drainage, cohesion and deformation. By locating where these profiles intersect, the analysis defines the precise areas most like to reactivate under stress, lose shear strength and thus, requires targetted reinforcement26.

24 Astorga Gättgens, A., Mende, A. and Piedra, M. (2005) Aplicación del Índice de Fragilidad Ambiental (IFA) en el Plan Regulador del cantón de Escazú y su área rural, según la metodología de la SETENA: Informe de Síntesis Final. San José: Municipalidad de Escazú. 25 Quesada-Román, A. (2024) 'Mass movements dynamics and morphologies in Costa Rica', in Quesada-Román, A. (ed.) Landscapes and Landforms of Costa Rica. Cham: Springer (World Geomorphological Landscapes), pp. 79–94. doi: 10.1007/978-3-03164940-0_4. 26 Soil Survey Staff (2014) Keys to Soil Taxonomy. 12th edn. Washington, DC: United States Department of Agriculture, Natural Resources Conservation Service.

1500 m

Section 3-3 43

Hydrology Network

Domain chapter

Water in Escazu forms a continuous surface and subsurface system where flow characteristics Figure 1.28 Hydrological network details change drastically with the terrain. In steep southern mountains, mapping reveals a critical absence of acquifiers, thus the water either accumulates in zones without proper drainage or runoff in the lower plateau and unrbanised valleys27. Unmanaged watershed concentrates with considerable energy, causing sever soil saturation triggering the breakages. Instability often develops where water accumulates invisibly beneath the surface, shifting the hydrological focus from isolated rivers to a comprehensive field of flow. The streams forms seasonally to channel water during months of high precipitation, however fails in cases of sudden cloud bursts. Understanding these specific saturation dynamics dictates where water must be slowed, redirected and kept away from vulnerable ground to mitigate slope failure.

27 Astorga Gättgens, A., Mende, A. and Piedra, M. (2005) Aplicación del Índice de Fragilidad Ambiental (IFA) en el Plan Regulador del cantón de Escazú y su área rural, según la metodología de la SETENA: Informe de Síntesis Final. San José: Municipalidad de Escazú.

Figure 1.29 Hydrological networks and aquifer distribution in Escazú

Domain chapter

Conclusion

The case study demonstrates that addressic dynamic landslide risks require a comprehensive syn- Figure 1.30 Site analysis conclusion thesis of multiple environmental layers. By integrating data on climate patterns, topographical vari- framework ations, soil mapping, ecological corridor with its biodiversity and most importantly the hydrological network, a spatial understanding of the landscape’s underlying vulnerabilities is put forth. These layers of analysis become important for any intervention framework of an active design plan.

Figure 1.31 Different moments converging during Landslides

The ultimate challenge lies in how the water is managed and regulation of the saturation trigger. This dictates a corresponding heirarchy of stabilisation layers. Altogether, this becomes the base of understanding how design can coexist with a dynamic landscape.

Case Study Community Development

Mesopotamia: Distributed Interconnection In the low-rainfall landscapes of southern Mesopotamia, in present-day central and southern Iraq, urban and agricultural development depended on the construction and continuous maintenance of irrigation canals28. These waterways were not isolated conduits but extended networks linking cultivated land, settlements, transport routes, and sites of resource extraction; river branches and canals further shaped the location of harbours, markets, and settlement edges, making the hydraulic network inseparable from urban life29. Communities thus operated as mutually dependent parts of a shared system, since the productivity and water security of any one area depended on the condition of channels elsewhere in the network. The transferable principle, shown in Figure 1.33, is therefore not irrigation itself but distributed interconnection: water infrastructure understood as a collective spatial network rather than a single centralised object. For Escazú, this implies managing runoff through a hierarchy of interconnected Figure 1.33 The canal systems of Hanging channels, interception points, and retention areas distributed across the watershed, where each Gardens, Mesopotamia intervention is executed locally while contributing to the stability and productivity of the wider territory.

Egypt: Seasonal Retention and Coordination

Case Study

Egyptian basin irrigation embodied a different relationship with water. Rather than distributing it continuously, the system worked with the seasonal rise and recession of the Nile: earthen embankments divided the floodplain into basins that admitted, retained, and later released floodwater30. Holding the water saturated the soils and allowed river sediment to settle across the cultivated surface, preparing the land for planting once the flood receded, and so converting a recurring hydrological event into an organised agricultural sequence. Community organisation emerged as much through the coordination of time as through the division of land, since the opening, retention, and draining of each basin required decisions extending beyond individual plots31. As shown in Figure 1.34, the transferable principle is retention before discharge: floodwater was slowed, temporarily stored, and made productive rather than immediately removed. In Escazú, this logic could inform a sequence of vegetated retention pockets, ter- Figure 1.34 Nile basin irrigation and raced cultivation, and controlled drainage routes that receive runoff before releasing it gradually settlement downslope.

Chinampas: A Productive Water–Land Matrix

Introduction: Historical Evolution of Urbanisation using water Management Water management has historically operated as more than a technical response to flooding, drought Figure 1.32 Urban models using water or agricultural demand. It has also provided the spatial and organisational framework through management - Mesopotamia, Egypt, Aztec which settlements expand, resources are distributed, and communities become interdependent. The Mesopotamian canal networks, Egyptian basin-irrigation systems, and chinampas of the Basin of Mexico demonstrate three distinct relationships between water and collective development. Their relevance lies not in reproducing their historical forms, but in understanding how hydraulic infrastructure can structure movement, productivity, maintenance and habitation. As illustrated in Figure 1.32, the comparison focuses on three transferable operations—interconnection, retention and productive integration—that can inform an adaptive territorial strategy for Escazú.

28 Jotheri, J., Rokan, M., Al-Ghanim, A., Rayne, L., de Gruchy, M. and Alabdan, R. (2025) 'Identifying the preserved network of irrigation canals in the Eridu region, southern Mesopotamia', Antiquity, 99(405), article e20. doi: 10.15184/aqy.2025.19. 29 Emberling, G. (2015) 'Mesopotamian cities and urban process, 3500–1600 BCE', in Yoffee, N. (ed.) The Cambridge World History: Early Cities in Comparative Perspective, 4000 BCE–1200 CE. Cambridge: Cambridge University Press, pp. 253–278. doi: 10.1017/ CHO9781139035606.016. 30 Butzer, K.W. (1976) Early Hydraulic Civilization in Egypt: A Study in Cultural Ecology. Chicago: University of Chicago Press. 31 Haug, B. (2017) 'Water and power: Reintegrating the state into the study of Egyptian irrigation', History Compass, 15(10), article e12394. doi: 10.1111/hic3.12394.

The chinampa system of the Basin of Mexico demonstrates the closest integration of water management, cultivation, and settlement32. Though commonly described as “floating gardens,” chinampas were raised fields built from lake sediment and organic material within shallow wetlands, separated by canals that supplied water, supported transport, and provided nutrient-rich sediment for return to the cultivated surface33. Combined with aqueducts and causeways, this field-andcanal matrix kept food production, movement, and water circulation closely tied to the growth of surrounding settlements. The chinampa precedent is therefore interpreted in Figure 1.35 as a multifunctional productive interface in which water is the medium connecting inhabitation and agriculture rather than a separated element. Its relevance to Escazú lies in coupling hydrological management with productive Figure 1.35 Aztec chinampas floating garden planting, community access, and ecological repair, so that vegetated drainage channels and agricultural terraces contribute simultaneously to runoff control, soil reinforcement, food production, and shared space — allowing community development to support territorial stabilisation rather than compete with it. 32 Rey-Hernández, C. and Bobbink, I. (2022) 'Chinampas agriculture and settlement patterns: The contemporary relevance of Aztec floating gardens', Blue Papers, 1(2), pp. 90–99. doi: 10.58981/bluepapers.2022.2.09. 33 Morehart, C.T. and Frederick, C. (2014) 'The chronology and collapse of pre-Aztec raised-field (chinampa) agriculture in the northern Basin of Mexico', Antiquity, 88(340), pp. 531–548. doi: 10.1017/S0003598X00101164.

Case Study

Synthesis - Hydrological Branching System The comparative analysis identifies a shared logic in which water-management systems organise land use, productivity, circulation, and collective occupation through interconnected hierarchies. As shown in Figure 1.36, these principles are translated through the fractal network model of Geoffrey B. West, James H. Brown, and Brian J. Enquist34, using primary, secondary, and tertiary branches to distribute flow across different territorial scales. Within Escazú, this logic informs a hydrological branching algorithm in which main channels redirect runoff, smaller branches support infiltration and soil stabilisation, and terminal nodes define areas for vegetation, cultivation, infrastructure, and settlement. The fractal network therefore acts as the spatial mechanism connecting water management, ecological reinforcement, and community development within an adaptive agro-urban system. 34 West, G.B., Brown, J.H. and Enquist, B.J. (1997) 'A general model for the origin of allometric scaling laws in biology', Science, 276(5309), pp. 122–126. doi: 10.1126/science.276.5309.122.

Figure 1.36 Algorythmic branching sequence for water dissipation

Soil Stabilisation

China: Rice Terraces The Hani Rice Terraces in Yunnan Province, China, form a landscape-scale ecological system distributed along steep mountainous slopes. It integrates dense forests, villages, river valleys and artificial wetland terraces35. This vertical spatial structure performs critical ecological functions, including soil and water conservation and the mitigation of floods by storing excess water in paddy fields. Furthermore, mixed forests on steep lands control soil erosion and reduce the risk of landslides. Ditches are utilised to catch flows and deposit sediment before it enters the terrace. However, as an individual intervention, the system faces distinct vulnerabilities. The steep, deeply-cut mountainous terrain remains highly susceptible to soil and water loss, leading to modern landslides and terrace collapses, particularly within the soil layers rather than weathered rock. Additionally, without upper-region reservoirs, the terraces are severely threatened by droughts.

Figure 1.38 Cascaded terraces

Hong Kong - Soil Nailing

One image (not system logic diagram) of any one of the soil stabilisation methods

Soil nailing is a robust structural engineering method introduced in Hong Kong to stabilise steep, man-made slopes36. The technique involves installing high-yield steel reinforcing bars into the ground through drilling and grouting, often connected at the surface by a grillage. Installed at close spacing, these nails bind the soil into an integral mass, making the slope highly resilient to adverse ground conditions and undetected weak geological zones. Despite its versatility, soil nailing can fail or prove insufficient when applied as a standalone operation without complementary measures. For instance, soil nails alone cannot always stop surface erosion or manage intense surface water runoff, which is why they must be combined with proper drainage provisions, hard surfacing or landscaping covers to fully secure the slope and prevent gradual degradation.

Case Study

Figure 1.39 Engineered soil nailing

Landslide Control - using Geocells Geocell slope reinforcement is a stabilisation intervention primarily designed for shallow surface confinement37. Operating at a much shallower depth compared to structural methods like soil nailing, geocells provide a stable matrix that confines topsoil and prevents immediate surface erosion. By creating a cellular confinement network, they modify the slope geometry at the surface level to maintain soil resistance against the erosive forces of water and gravity. However, when used strictly as an individual operation, geocells have notable limitations. Because their primary function is limited to shallow surface confinement, they do not anchor into the deeper, stable bedrock. Consequently, if a slope is subjected to deeper rotational failures, internal liquefaction or massive deep-seated instability, the geocell layer is easily bypassed or entirely displaced, highlighting its failure when deep structural reinforcement is genuinely required.

Introduction: Existing Intervention for soil stabilisation Soil stabilization is examined through distinct systems operating at different depths and territo- Figure 1.37 Soil stabilisation intervention at rial scales: the rice terraces of China, geocell slope reinforcement, the soil-nailing systems used present extensively in Hong Kong, and rooting plantation systems. Rather than treating these precedents as isolated engineering solutions, this comparison evaluates how each intervention modifies the interaction between slope geometry, water movement, and soil resistance. The cases process hydrological management to shallow surface confinement and deeper structural reinforcement. Through these case studies different stabilisation techniques are analysed on how they can be implemented. It delivers a guide of bioremediation tools that are fit for controling rupturing soil, erosion, thus allowing to preserve and protect fragile landscape.

35 People's Government of Honghe Prefecture (2010) Hani Rice Terraces System, Yunnan, China: Proposal for a Globally Important Agricultural Heritage System (GIAHS). Rome: Food and Agriculture Organization of the United Nations. 36 Choi, K.Y. and Cheung, R.W.M. (2013) 'Landslide disaster prevention and mitigation through works in Hong Kong', Journal of Rock Mechanics and Geotechnical Engineering, 5(5), pp. 354–365. doi: 10.1016/j.jrmge.2013.07.007. 37 Biabani, M.M. and Nimbalkar, S. (2026) 'Assessment of geocell confinement for sustainable stabilisation of a landslide-affected road: Australian case study', Sustainability, 18(17), article 9004. doi: 10.3390/su18179004.

Figure 1.40 Erosion stabilisation through confined surface treatment

Bioremediation Forestry - Plant rooting systems Root systems should be understood as a form of living structural engineering. Mature roots increase soil shear resistance by mobilising tensile forces across potential failure surfaces. However, this contribution is not uniform and varies according to root-area ratio, tensile strength, branching morphology, soil moisture and rooting depth38. Fibrous roots reinforce shallow soil, deeper woody roots can cross potential shear planes, and rhizomatic systems distribute reinforcement laterally across the slope39. Bamboo is relevant because its connected rhizome system can establish a dense reinforcement network within the upper soil profile40. While evergreen canopies sustain dry-season transpiration, keeping soil suction high before the first rains. Altogether it stabilises slopes in two complementary ways: dense fine roots bind the upper soil into a cohesive mat, while deep taproots anchor that Figure 1.41 Plant roots holding soil mat across the potential slip surface. 38 DiBiagio, A., Capobianco, V., Oen, A. and Tallaksen, L.M. (2024) 'State-of-the-art: Parametrization of hydrological and mechanical reinforcement effects of vegetation in slope stability models for shallow landslides', Landslides, 21, pp. 2417–2446. doi: 10.1007/s10346-024-02300-1. 39 Stokes, A., Atger, C., Bengough, A.G., Fourcaud, T. and Sidle, R.C. (2009) 'Desirable plant root traits for protecting natural and engineered slopes against landslides', Plant and Soil, 324(1–2), pp. 1–30. doi: 10.1007/s11104-009-0159-y. 40 Kaushal, R., Singh, I., Thapliyal, S.D., Gupta, A.K., Mandal, D., Tomar, J.M.S., Kumar, A., Alam, N.M., Kadam, D., Singh, D.V., Mehta, H., Dogra, P., Ojasvi, P.R. and Reza, S. (2020) 'Rooting behaviour and soil properties in different bamboo species of Western Himalayan Foothills, India', Scientific Reports, 10, article 4966. doi: 10.1038/s41598-020-61418-z.

Case Study

Bioremediation Interventions No single technique is sufficient: structural barriers provide immediate resistance, geotextile or cellular reinforcement maintains surface cohesion during establishment and vegetation progressively consolidates the soil over longer timescales. Thus relying on a single method of stabilisation is rarely sufficient. As isolated interventions fail outside its specific scope, a truly resilient strategy requires a culmination of diverse approaches. A successful model requires careful calibration, mixed responses tailored based on the regional needs. To safely and effectively deploy these interventions, a multi-layered model integration is necessary considering defence against erosion and slope failure. 54

Figure 1.42 Gradient integrated stabilisation system framework

Case Study

Research Questions

Determining the core trigger and different stabilisation factors for both the terrain and urban Figure 1.43 Research question framework growth, the research questions how to generate an adaptive, resilient settlement that transform landslide-prone territories into landscapes of managed coexistence. To address this objective, the study is structured around three interconnected enquiries. First, it examines how gradient systems can establish spatial settlement patterns using soft boundaries. Second, it explores the hydrological behaviors required to regulate and influence terrain stability. Finally, it evaluates how locally sourced waste materials can be formulated into responsive compositions to meet specific hydrological needs. By systematically analyzing gradient-based morphogenesis, hydrological regulation and responsive material systems, the project seeks to establish site-responsive methodologies for sustainable territorial stabilization 56

Hypothesis

Domain chapter

“Conventional approaches to landslide risk management largely regard landslides as hazards that must be prevented or avoided. However, this research hypothesizes that it’s possible to develop adaptive settlement framework that can coexist with and accommodate landslide dynamics”

CHAPTER BIBLIOGRAPHY

CONCLUSION

Domain chapter

This critique defines the design problem of the project. The project cannot rely on a single act of protection, nor can it assume that all unstable areas should be treated in the same way. Instead, it must establish where movement should remain unobstructed, where the terrain can be gradually reinforced, and where occupation must be restricted or carefully introduced. Architecture is therefore repositioned as a territorial instrument rather than an isolated object: one that reorganises water, stabilisation, ecology, and settlement through a phased and differentiated system. The chapter shifts the research from documenting landslide susceptibility to confronting the spatial decisions through which risk is either reduced.

Highland, L.M. and Bobrowsky, P. (2008) The Landslide Handbook — A Guide to Understanding Landslides, Circular 1325. Reston, VA: U.S. Geological Survey.

Froude, M.J. and Petley, D.N. (2018) 'Global fatal landslide occurrence from 2004 to 2016', Natural Hazards and Earth System Sciences, 18(8), pp. 2161–2181. doi: 10.5194/nhess-18-2161-2018.

U.S. Geological Survey (2026) 2026 Nepal Debris Avalanche and Flash Flood. Landslide Hazards Program. Available at: https://www.usgs.gov/programs/landslide-hazards/science/2026-nepal-debris-avalanche-and-flash-flood (Accessed: 15 September 2026).

Quesada-Román, A. (2024) 'Mass movements dynamics and morphologies in Costa Rica', in Quesada-Román, A. (ed.) Landscapes and Landforms of Costa Rica. Cham: Springer (World Geomorphological Landscapes), pp. 79–94. doi: 10.1007/978-3-031-64940-0_4.

Quesada-Román, A. (2021) 'Landslide risk index map at the municipal scale for Costa Rica', International Journal of Disaster Risk Reduction, 56, article 102144. doi: 10.1016/j.ijdrr.2021.102144.

Hungr, O., Evans, S.G., Bovis, M.J. and Hutchinson, J.N. (2001) 'A review of the classification of landslides of the flow type', Environmental & Engineering Geoscience, 7(3), pp. 221–238. doi: 10.2113/gseegeosci.7.3.221.

Fan, X., Xu, Q., Scaringi, G., Li, S. and Peng, D. (2017) 'A chemo-mechanical insight into the failure mechanism of frequently occurred landslides in the Loess Plateau, Gansu Province, China', Engineering Geology, 228, pp. 337– 345. doi: 10.1016/j.enggeo.2017.09.003.

Hu, W., Scaringi, G., Xu, Q., Van Asch, T.W.J., Huang, R. and Han, W. (2018) 'Suction and rate-dependent behaviour of a shear-zone soil from a landslide in a gently-inclined mudstone-sandstone sequence in the Sichuan basin, China', Engineering Geology, 237, pp. 1–11. doi: 10.1016/j.enggeo.2018.02.005.

Laimer, H.J. (2017) 'Anthropogenically induced landslides — A challenge for railway infrastructure in mountainous regions', Engineering Geology, 222, pp. 92–101. doi: 10.1016/j.enggeo.2017.03.015.

10.

Stokes, A., Norris, J.E., van Beek, L.P.H., Bogaard, T.A., Cammeraat, L.H., Mickovski, S.B., Jenner, A., Di Iorio, A. and Fourcaud, T. (2008) 'How vegetation reinforces soil on slopes', in Norris, J.E. et al. (eds.) Slope Stability and Erosion Control: Ecotechnological Solutions. Dordrecht: Springer, pp. 65–118. doi: 10.1007/978-1-4020-6676-4_4.

11.

Quesada-Román, A. (2022) 'Disaster risk assessment of informal settlements in the Global South', Sustainability, 14(16), article 10261. doi: 10.3390/su141610261.

12.

Tarolli, P. and Straffelini, E. (2020) 'Agriculture in hilly and mountainous landscapes: Threats, monitoring and sustainable management', Geography and Sustainability, 1(1), pp. 70–76. doi: 10.1016/j.geosus.2020.03.003.

13.

Centeno-Morales, J., Vargas-Trejos, Y., González-Varela, M. and Alfaro-Orozco, E. (2020) 'Estudio de caso: Lajas Compartir, un acercamiento al desplazamiento ambiental en Costa Rica', Revista Geográfica de América Central, 64(1), pp. 179–201. doi: 10.15359/rgac.64-1.7.

14.

Arias, M., Fuentes, O. and Fallas, J. (2011) 'Criterios utilizados para la microzonificación de la microcuenca Lajas, Cantón de Escazú, Costa Rica', En Torno a la Prevención, 7, pp. 1–7.

15.

Quesada-Román, A. (2021) 'Landslide risk index map at the municipal scale for Costa Rica', International Journal of Disaster Risk Reduction, 56, article 102144. doi: 10.1016/j.ijdrr.2021.102144.

Domain chapter

The domain study reveals that instability in Escazú is not simply a physical condition of the slope, but a consequence of how environmental processes have been interrupted, redirected, and occupied. The mapping shows that risk is produced through uneven relationships between water movement, weakened ground, and urban expansion. It also exposes the limits of conventional planning, which often reduces complex territorial conditions to fixed categories of safe and unsafe land. Such boundaries may identify danger, but they do not explain how vulnerability is continually reproduced through drainage failure and the occupation of active hydrological corridors.

Costa Rica (1976) Decreto Ejecutivo No. 6112-A: Creación de la Zona Protectora Cerros de Escazú, 17 July. San José: La Gaceta.

32.

Rey-Hernández, C. and Bobbink, I. (2022) 'Chinampas agriculture and settlement patterns: The contemporary relevance of Aztec floating gardens', Blue Papers, 1(2), pp. 90–99. doi: 10.58981/bluepapers.2022.2.09.

17.

Sistema Nacional de Áreas de Conservación (SINAC) (2015) Plan General de Manejo de la Zona Protectora Cerros de Escazú 2015–2025. San José: SINAC, Ministerio de Ambiente y Energía.

33.

Morehart, C.T. and Frederick, C. (2014) 'The chronology and collapse of pre-Aztec raised-field (chinampa) agriculture in the northern Basin of Mexico', Antiquity, 88(340), pp. 531–548. doi: 10.1017/S0003598X00101164.

18.

Lann, T., Bao, H., Lan, H., Zheng, H., Yan, C. and Peng, J. (2024) 'Hydro-mechanical effects of vegetation on slope stability: A review', Science of the Total Environment, 926, article 171691. doi: 10.1016/j.scitotenv.2024.171691.

34.

West, G.B., Brown, J.H. and Enquist, B.J. (1997) 'A general model for the origin of allometric scaling laws in biology', Science, 276(5309), pp. 122–126. doi: 10.1126/science.276.5309.122.

19.

Piedra González, M.A. (2006) Estudio social en la zona agrícola del cantón de Escazú. Informe del proceso de formulación del Plan Regulador para la Zona Agrícola del Cantón de Escazú. Escazú: Municipalidad de Escazú.

35.

20.

Municipalidad de Escazú (2025) Plan Regulador del Cantón de Escazú. Escazú: Municipalidad de Escazú.

People's Government of Honghe Prefecture (2010) Hani Rice Terraces System, Yunnan, China: Proposal for a Globally Important Agricultural Heritage System (GIAHS). Rome: Food and Agriculture Organization of the United Nations.

21.

Sistema Nacional de Áreas de Conservación (SINAC) (2015) Plan General de Manejo de la Zona Protectora Cerros de Escazú 2015–2025. San José: SINAC, Ministerio de Ambiente y Energía.

36.

Choi, K.Y. and Cheung, R.W.M. (2013) 'Landslide disaster prevention and mitigation through works in Hong Kong', Journal of Rock Mechanics and Geotechnical Engineering, 5(5), pp. 354–365. doi: 10.1016/j.jrmge.2013.07.007.

37.

Biabani, M.M. and Nimbalkar, S. (2026) 'Assessment of geocell confinement for sustainable stabilisation of a landslide-affected road: Australian case study', Sustainability, 18(17), article 9004. doi: 10.3390/su18179004.

38.

DiBiagio, A., Capobianco, V., Oen, A. and Tallaksen, L.M. (2024) 'State-of-the-art: Parametrization of hydrological and mechanical reinforcement effects of vegetation in slope stability models for shallow landslides', Landslides, 21, pp. 2417–2446. doi: 10.1007/s10346-024-02300-1.

Domain chapter

22. 23.

Soil Survey Staff (2014) Keys to Soil Taxonomy. 12th edn. Washington, DC: United States Department of Agriculture, Natural Resources Conservation Service.

24.

Astorga Gättgens, A., Mende, A. and Piedra, M. (2005) Aplicación del Índice de Fragilidad Ambiental (IFA) en el Plan Regulador del cantón de Escazú y su área rural, según la metodología de la SETENA: Informe de Síntesis Final. San José: Municipalidad de Escazú.

39.

Stokes, A., Atger, C., Bengough, A.G., Fourcaud, T. and Sidle, R.C. (2009) 'Desirable plant root traits for protecting natural and engineered slopes against landslides', Plant and Soil, 324(1–2), pp. 1–30. doi: 10.1007/s11104-0090159-y.

40.

Kaushal, R., Singh, I., Thapliyal, S.D., Gupta, A.K., Mandal, D., Tomar, J.M.S., Kumar, A., Alam, N.M., Kadam, D., Singh, D.V., Mehta, H., Dogra, P., Ojasvi, P.R. and Reza, S. (2020) 'Rooting behaviour and soil properties in different bamboo species of Western Himalayan Foothills, India', Scientific Reports, 10, article 4966. doi: 10.1038/s41598020-61418-z.

25.

Meteoblue (2026) Climate and weather history: Escazú, Costa Rica. Available at: https://www.meteoblue.com (Accessed: 10 May 2026).

26.

Soil Survey Staff (2014) Keys to Soil Taxonomy. 12th edn. Washington, DC: United States Department of Agriculture, Natural Resources Conservation Service.

27.

28.

Jotheri, J., Rokan, M., Al-Ghanim, A., Rayne, L., de Gruchy, M. and Alabdan, R. (2025) 'Identifying the preserved network of irrigation canals in the Eridu region, southern Mesopotamia', Antiquity, 99(405), article e20. doi: 10.15184/aqy.2025.19.

29.

Emberling, G. (2015) 'Mesopotamian cities and urban process, 3500–1600 BCE', in Yoffee, N. (ed.) The Cambridge World History: Early Cities in Comparative Perspective, 4000 BCE–1200 CE. Cambridge: Cambridge University Press, pp. 253–278. doi: 10.1017/CHO9781139035606.016.

30.

Butzer, K.W. (1976) Early Hydraulic Civilization in Egypt: A Study in Cultural Ecology. Chicago: University of Chicago Press.

31.

Haug, B. (2017) 'Water and power: Reintegrating the state into the study of Egyptian irrigation', History Compass, 15(10), article e12394. doi: 10.1111/hic3.12394.

Domain chapter

16.

Methodology

METHODOLOGY

METHODOLOGY OVERVIEW

Methodology

The project is structured as an iterative process moving through interconnected stages of analysis, optimisation, and making. Rather than treating the landscape as a fixed surface, the research reads it as a dynamic system shaped by environmental processes. Each stage develops from the observed behaviour of the site and contributes to defining where intervention is required, how occupation can emerge, and how the system may adapt over time. Methodology

The process begins by establishing a phased territorial strategy and mapping the gradients that characterise the watershed. These gradients provide the basis for hydrological simulation, where a Height-Field model reveals patterns of movement and accumulation. The resulting behaviour informs a branching network that is subsequently tested and refined according to its ability to redistribute water and reduce erosive intensity. This territorial framework then guides the spatial development of the settlement. Rule-based distribution and vector fields transform programme relationships into evolving morphologies, while structural analysis is used to refine both permanent and lightweight systems according to their performance. The drainage network is similarly developed through tessellation and structural testing, linking geometric organisation with fabrication and mechanical behaviour. Material-hydrology experiments extend this process into physical testing, examining how different material responses can support water management and soil stabilisation. These experiments, together with the evaluation of channel prototypes, feed back into the wider design process. The methodology therefore operates as a continuous loop in which territorial analysis informs computation, computation directs physical intervention, and testing generates the evidence required for further refinement.

Gradient Mapping

Height-Field Model

Gradient mapping establishes the primary spatial reading of the watershed, interpreting the territory as a continuous field of changing conditions rather than a set of fixed zones. Multiple site parameters are combined, weighted, and prioritised within a single framework to produce a gradient of environmental intensity. This identifies areas where intervention should be concentrated and establishes a spatial hierarchy for subsequent stages of the methodology. slope

Methodology

rivers

Methodology

The resulting gradient defines how conditions vary across the watershed and provides a common spatial reference for later computational processes. Particle simulation, branching networks, and spatial distribution models can therefore respond to the relative intensity of each area rather than applying uniform interventions. Gradient mapping consequently translates site analysis into an operational framework that informs the location, intensity, and adaptation of design strategies1.

LEGEND height

precipitation

soil type

debris river water sediment

Selection parameter High

Figure 2.2 Collected data for simulation

Low

Figure 2.1 Gradient mapping of Escazú

1 Forman, Richard T. T. Land Mosaics: The Ecology of Landscapes and Regions. Cambridge: Cambridge University Press, 1995.

Developed in Houdini, the height-field simulation translates the topography of Escazú into a dynamic model of hydrological movement. Particles are released across the 3D terrain and respond to changing slopes, tracing how rainfall moves through the watershed before concentrating within the site. The simulation reveals where flows accelerate, branch, converge, and accumulate, providing a temporal reading of water behaviour that gradient mapping alone cannot capture. The model also calculates the relative energy of each flow from its velocity and movement across the terrain, distinguishing lower-energy areas of retention from higher-energy corridors with greater erosion and sediment transport potential. These trajectories inform the branching algorithm and the placement of the drainage network. The simulation is then repeated with the proposed channels in place to validate their performance, testing whether they redistribute water, reduce flow concentration, and lower erosive energy across the slope. In this way, the height-field operates both as an analytical tool and as a feedback mechanism for refining the hydrological intervention2. 2 McHarg, Ian L. Design with Nature. Garden City, NY: Natural History Press, 1969.

Branching Algorithm The Diffusion-Limited Aggregation (DLA)

Cellular Automata (CA)

Methodology

Building on the hydrological trajectories generated in Houdini, the algorithm produces a respon- Figure 2.3 Channels & branching algorithm sive network that follows the movement and intensity of water across the terrain. The proposed branching-channel strategy is derived from existing topographic conditions and the environmental factors identified during preliminary investigation. It reorganises surface runoff, reduces concentrated flow and erosion, and thereby lowers the likelihood and severity of catastrophic failure, redirecting potential slope instability towards a more gradual and manageable geomorphological process. The network defines where water should be intercepted, redistributed, slowed, or allowed to continue, while identifying connections between stabilised areas and future zones of occupation. Its value lies not in reproducing the appearance of natural branching, but in adopting its capacity to adapt, distribute resources, and respond efficiently to changing conditions3. 3 Wolfram, Stephen. A New Kind of Science. Champaign, IL: Wolfram Media, 2002.

Figure 2.4 Cellular automata of spatial distribution

Cellular automata is used to translate the spatial framework into a rule-based pattern of occupation. The site is divided into cells that respond to neighbouring conditions, allowing program to grow incrementally rather than being imposed as fixed zones. Local rules control adjacency, cluster size, and separation, producing distributed functional islands that remain connected to the wider territorial system. The method is used to test how occupation can expand while maintaining soft boundaries between different functions and avoiding areas where development would conflict with hydrological or ecological processes. Through successive iterations, the cellular system generates alternative patterns of growth and provides a basis for locating and refining future community development4. 4 Batty, Michael. The New Science of Cities. Cambridge, MA: MIT Press, 2013.

Structural Analysis - Karamba Vector Diffussion

3 x 6 UV - Adjustment

Utilisation Output Compression

-90.4% -78.7%

Methodology

-54.1% -29.4% -14.7% 0.0% 10.3%

Maximum Displacement 0.337cm

30.7% 54.7% 72.8%%

Tension

Directional relationships between neighbouring programme areas are diffused across the site to Figure 2.5 Vector diffussion addition transform the discrete cellular distribution into a continuous spatial field. This allows each cluster to respond to surrounding functions and to the larger environmental structure rather than remaining as an isolated cell. The resulting field guides the orientation, extension, and interaction of spatial clusters, producing smoother transitions between programmes while preserving their functional relationships. It therefore acts as a bridge between programmatic distribution and morphological development, allowing settlement form to emerge from both local adjacency and territorial conditions5.

Figure 2.6 3 x 6 UV - tessellation adjustment

95.2%

Structural performance of the drainage channel is evaluated through Karamba to test how tessellation affects stress distribution and deformation. The analysis compares the behaviour of the continuous channel geometry with segmented configurations, identifying areas of higher structural demand and locations where subdivision can reduce unsupported spans. The results are used to refine the position of tessellation joints and the proportions of individual components. In this way, structural analysis becomes part of the geometric development of the channel, ensuring that its modular configuration responds to mechanical performance rather than fabrication logic alone.

5 Spirn, Anne Whiston. The Granite Garden: Urban Nature and Human Design. New York: Basic Books, 1984.

Computational Fluid Dynamics

Machine Learning

try

Methodology

W av ele

ng th

ym As

Change in Velocity

+ Dataset (dependent variable)

Micro-analysis of channel behaviour and diversion characteristics establishes the logical frame- Figure 2.7 Fluid dynamics study - small scale work that governs the branching network. From simulated data, a surrogate model is developed to determine channel profile, cross-sectional shape, and angles of diversion, and to predict how the introduction of a channel alters the local rate of flow — allowing these changes to be modelled before construction. A catalogue of experiments records the influence of channel profile, width, depth, and diversion angle on flow behaviour. A subsequent series testing clusters of meanders determines how asymmetry, amplitude, and wavelength affect flow. Together these define the micro-conditions of the channels and calibrate the branching network at the scale of the individual conduit. 74

Figure 2.8 Data set of small scale experiments

Applied directly to the branching algorithm, the machine-learning model studies how the network grows, divides, and adapts under different input conditions. By learning from multiple generated iterations, it identifies recurring relationships between the algorithm’s parameters and the resulting spatial configurations, moving beyond the reproduction of existing outputs to generate speculative variations derived from the internal logic of the system. Through this process the model anticipates how the algorithm might behave under conditions not yet explicitly tested, revealing alternative network formations, possible future adaptations, and spatial relationships unlikely to emerge through manual adjustment. Machine learning thus acts as an exploratory extension of the branching algorithm, expanding its design potential rather than determining a final solution.

Optimisation - Graphic Statics

Methodology

FEA Optimisation - Ameba

FEA optimisation through Ameba is applied to the permanent structural morphologies to improve Figure 2.9 FEA-based structural optimisation their performance under potential landslide loading. The process identifies areas of low structural of permanent building typologies demand where material can be removed, while reinforcing zones that carry higher stresses. This produces a more materially efficient morphology while strengthening the overall structure against lateral and impact forces generated by slope movement6.

6 Preisinger, Clemens. “Linking Structure and Parametric Geometry.” Architectural Design 83, no. 2 (2013): 110–113. https://doi. org/10.1002/ad.1564.

Figure 2.10 Graphic statics optimisation of lightweight bamboo structures

Graphic statics is used to optimise the lightweight bamboo structures by aligning their geometry with the internal flow of forces. The method identifies efficient load paths, allowing members to be positioned and dimensioned according to structural demand while reducing unnecessary material. This produces a lightweight structural system that remains adaptable, materially efficient, and suitable for rapid assembly and disassembly in areas of greater environmental uncertainty7.

7 Akbarzadeh, Masoud, Tom Van Mele, and Philippe Block. “On the Equilibrium of Funicular Polyhedral Frames and Convex Polyhedral Force Diagrams.” Computer-Aided Design 63 (2015): 118–128. https://doi.org/10.1016/j.cad.2015.01.006.

Methodology

Material Hydrology

The physical experiments investigate how different material behaviours can work with water as Figure 2.11 Material experimentation part of the broader soil-stabilisation strategy. Hydrophobic and hydrophilic mixtures are compared hydrophobic & hydrophilic to understand how variations in composition affect their interaction with moisture, durability, and suitability for different environmental conditions.

Figure 2.12 Material experimentation hydrophobic & hydrophilic

Rather than defining a single material solution, the methodology develops a range of compositions with different hydrological responses. These results are then related back to site conditions, informing where each material behaviour can contribute most effectively to water management and soil stabilisation.

The research develops through iterative casting and testing, allowing the material system to be refined according to its intended role within the landscape. Hydrophobic compositions are also tested through tessellated channel prototypes, linking material performance with geometry, assembly, and water movement8.

8 Brooks, Robert M. “Soil Stabilization with Fly Ash and Rice Husk Ash.” International Journal of Research and Reviews in Applied Sciences 1, no. 3 (2009): 209–217.

Forman, Richard T. T. Land Mosaics: The Ecology of Landscapes and Regions. Cambridge: Cambridge University Press, 1995.

McHarg, Ian L. Design with Nature. Garden City, NY: Natural History Press, 1969.

Wolfram, Stephen. A New Kind of Science. Champaign, IL: Wolfram Media, 2002.

Batty, Michael. The New Science of Cities. Cambridge, MA: MIT Press, 2013.

Spirn, Anne Whiston. The Granite Garden: Urban Nature and Human Design. New York: Basic Books, 1984.

Preisinger, Clemens. “Linking Structure and Parametric Geometry.” Architectural Design 83, no. 2 (2013): 110–113. https://doi.org/10.1002/ad.1564.

CONCLUSION

Akbarzadeh, Masoud, Tom Van Mele, and Philippe Block. “On the Equilibrium of Funicular Polyhedral Frames and Convex Polyhedral Force Diagrams.” Computer-Aided Design 63 (2015): 118–128. https://doi.org/10.1016/j. cad.2015.01.006.

The methodology develops a continuous process of simulating, optimising, and transforming the landscape. Hydrological behaviour is first mapped across the watershed and translated into a branching network responsive to water flow and terrain conditions. This framework informs the spatial distribution of ecological and architectural programmes, which are progressively developed through cellular automata, vector diffusion, and structural optimisation. The resulting systems are validated through the Height-Field model and refined through material-hydrology and fabrication testing to assess their capacity to manage water, reduce erosion, and stabilise the ground. The outcome is not a fixed solution, but an adaptive framework in which computational and physical testing continuously inform the next stage of development.

Brooks, Robert M. “Soil Stabilization with Fly Ash and Rice Husk Ash.” International Journal of Research and Reviews in Applied Sciences 1, no. 3 (2009): 209–217. Methodology

Methodology

CHAPTER BIBLIOGRAPHY

Research Development

RESEARCH DEVELOPMENT

Research Development

Research Development Previous environmental studies identified intense rainfall and concentrated runoff as the primary environmental drivers of flooding and slope instability in Escazú. During heavy rainfall events, water follows the steep topographic gradient, while the existing terrain does not provide sufficient drainage. This results in excessive water accumulation on vulnerable slopes. This phase therefore traces where water enters the study area, how it moves across the terrain, and where it accumulates, while evaluating the conditions under which existing flow paths may increase the risk of slope failure. In response, the proposed branching drainage system intercepts and redirects runoff before prolonged accumulation can saturate and destabilise vulnerable slopes. Rather than allowing water to concentrate along existing terrain depressions and natural drainage paths, the system enables earlier conveyance and discharge through multiple routes. By working with existing slopes and flow tendencies instead of imposing an independent geometry, the intervention aims to disperse water more effectively, reduce localised accumulation and improve drainage performance with minimal physical alteration to the landscape.

Research Development

Phase I Development

Figure 3.2 Territorial scope of the phase I intervention in Escazú

Rather than defining a fixed drainage solution, the intervention translates water behaviour into Figure 3.1 Intervention logic for a sitean adaptive channel system. Drawing conceptually on the adaptive water-foraging behaviour of responsive water-channel system branching root systems, its geometry responds to flow performance and topography, allowing channels to widen, divide, merge or terminate as runoff is conveyed, retained and redistributed across the site1.

1 Renton, M. and Poot, P. (2014). ‘Simulation of the evolution of root water foraging strategies in dry and shallow soils’, Annals of Botany, 114(4), pp. 763–778.

Site Selection Environmental and Hydrological Parameters

Selection preference

These are target areas for interventions

Independent factors

Research Development

These factors cannot be changed through any intervention even if they are direct causes of landslides

Dependent factors

These factors can be controlled through intervention and design choice

Selection parameter High

The analysis distinguishes between fixed environmental conditions that establish the underlying Figure 3.3 Environmental and hydrological susceptibility of Escazú and the hydrological conditions through which this becomes an active risk. criteria used to define intervention priorities Rainfall, topography, geology and soil are treated as existing constraints, while runoff concentration, water accumulation and surface-flow direction describe how water behaves across the terrain. These criteria are weighted according to their influence on flooding, drainage and slope instability2,3, as summarised in Figure 3.3. The weighted criteria are spatially overlaid to produce a map of intervention priority4. Areas where steep terrain, concentrated runoff and limited drainage capacity overlap require earlier water interception and redistribution, while areas with less convergence receive a lower priority, as shown in Figure 3.4. Rather than working against the terrain, the analysis identifies locations where existing slopes and flow tendencies can support effective water redirection with minimal physical modification.

2 Mora, S. and Vahrson, W.G. (1994) ‘Macrozonation methodology for landslide hazard determination’, Bulletin of the Association of Engineering Geologists, 31(1), pp. 49–58. 3 Montgomery, D.R. and Dietrich, W.E. (1994) ‘A physically based model for the topographic control on shallow landsliding’, Water Resources Research, 30(4), pp. 1153–1171. 4 Ayalew, L., Yamagishi, H. and Ugawa, N. (2004) ‘Landslide susceptibility mapping using GIS-based weighted linear combination: The case in Tsugawa area of Agano River, Niigata Prefecture, Japan’, Landslides, 1(1), pp. 73–81.

Low

Figure 3.4 Weighted intervention-priority map of Escazú

Intervention Zone Selection

Selected Region Selected Region

Area:12.45km²

Research Development

Area:12.45km²

LEGEND geological soil layer selected area

Figure 3.6 Intervention zone derived from the weighted priority analysis

Building on the weighted priority map, a combined area of 12.45 km² is selected for detailed in- Figure 3.5 Spatial extent of the selected vestigation. The selection focuses on locations where steep topography, concentrated runoff and intervention zones within the Escazú terrain insufficient drainage coincide, producing conditions in which water accumulation may increase slope instability. The three-dimensional study in Figure 3.5 positions these areas within the terrain and geological context, clarifying their spatial extent and relationship to the surrounding slopes.

Within this study area, the highest-priority cells are grouped into three intervention zones, as shown in Figure 3.6. Their boundaries follow the distribution of the identified risk conditions rather than a uniform geometric division. Each zone represents a different concentration of surface flow and drainage pressure to be examined through the following simulation. Together, they establish where runoff should be intercepted, redirected and dispersed before it accumulates on vulnerable slopes.

Pre-intervention Simulation Accumulation Patterns

Debris and water accumulation

Landslide Path

Research Development

precipitation

Area

12 %

Volume

5.20 m²

Most affected region

Plateau- existing urban space

soil

Area

16 %

Volume

6.58 m²

Research Development

water accumulation

LEGEND height debris river water sediment

debris accumulation

rivers

To test a rainfall-induced landslide scenario, the selected terrain was reconstructed as a Houdini HeightField model. Environmental parameters from the previous site study were converted into spatial masks: precipitation defines rainfall distribution, soil type controls surface erodibility, and existing rivers establish the principal drainage paths. The erosion process then simulates downslope water movement, material transport and deposition, producing separate flow and debris layers5.

5 Roudier, P., Peroche, E. and Perrin, M. (1993) ‘Landscapes synthesis achieved through erosion and deposition process simulation’, Computer Graphics Forum, 12(3), pp. 375–383.

Figure 3.7 Houdini HeightField simulation of rainfall-induced landslide and accumulation patterns

At the final simulation frame, water and displaced material were isolated to produce the two accumulation patterns shown in Figure 3.7. Water accumulation covers 12% of the study area, while debris accumulation covers 16%. These values describe their relative spatial extent rather than actual landslide probability. Mapping the flow and deposition patterns establishes a comparative baseline and identifies where water and debris converge most strongly6,7. 6 Wang, C., Li, S. and Esaki, T. (2008) ‘GIS-based two-dimensional numerical simulation of rainfall-induced debris flow’, Natural Hazards and Earth System Sciences, 8, pp. 47–58. 7 Hsu, Y.-C. and Liu, K.-F. (2019) ‘Combining TRIGRS and DEBRIS-2D models for the simulation of a rainfall infiltration-induced shallow landslide and subsequent debris flow’, Water, 11(5), Article 890.

Channel Performance Experiment Section Evaluation velocity velocity

Water channel types Water channel types

pressure pressure

50m

Water channel types

pressure

velocity

50m

90° 90°

Semicircle

1:2

Semicircle

50m

90°

Trapezoidal

Research Development

50m

90°

1:2

50m

90°

U - shape

1:2

50m

90°

U - shape

1:2

Figure 3.8 CFD comparison of channel cross-sections under identical flow conditions

Cross-sectional geometry directly influences velocity distribution within an open channel8. Figure 3.8 compares semicircular, trapezoidal and U-shaped sections under identical flow conditions. Within this test set, the semicircular profile produces the greatest reduction in flow velocity and the lowest relative pressure. It is therefore selected as the base section for the subsequent development of the channel system.

8 Han, Y., Li, T., Wang, S. and Chen, J. (2020) ‘A flow-measuring algorithm of arc-bottomed open channels through multiple characteristic sensing points of the flow-velocity sensor in agricultural irrigation areas’, Sensors, 20(16), Article 4504.

Channel Variables and Predictive Modelling

try

W av

ele

ng th

ym As

Change in velocity

Meander

Parameters

Change in Velocity

Research Development

+ Dataset (dependent variable)

Change in velocity

Angle

pe Slo

Parameters

Change in Velocity

+ Dataset (dependent variable)

To isolate the influence of geometry from changes in discharge, all simulations maintain the same Figure 3.9 Velocity and pressure responses flow condition while four channel variables are adjusted: meander configuration, angle in plane, to channel-geometry variables channel depth and divergence angle. Figure 3.9 records the spatial velocity and pressure fields produced by each variation. Rather than selecting a final form, these tests establish the hydraulic sensitivity of the design parameters and store each configuration as a matched set of geometric inputs and simulated outputs.

Figure 3.10 Regression models for meander and angle variables

The results indicate that velocity generally increases across the tested meander parameters, whereas angle produces a non-linear response that peaks within an intermediate range. Trained on the CFD dataset, the regression neural network predicts endpoint flow velocity for untested meander and angle configurations, avoiding a separate CFD simulation for every design option. This enables parameter combinations associated with lower endpoint velocity to be identified more efficiently. 97

Channel Network Agent Propagation Rules site bo unda ry

Width Formula ChannelWidth = max( WidthMinimum, WidthScaleCoefficient × Discharge ^ WidthExponent )

AGENT

Speed Calculation

existin g rive r

speed

width

alo ng enviro nmental fa ctor

speed

less branches

more branc hes

high s lope

low s lope

terrian

state

Maximum Rainfall Prediction In 20 years

Water source Energy> 3m/s

NewVelocity = √( max( 0, OldVelocity² + 2 × Gravity × HeightDrop ) ) − ManningRoughness × StepLength

Accumulating water Below breakage threshold

Branch flow distribution DischargeShare(branch) = cos( TurnAngle(branch) ) ÷ Σ cos( TurnAngle(all branches) )

Determination Branching velocity decay

Flood and erosion path

2. river

new branch Merged

140°

High breakage threshold

Research Development

BranchVelocity = EnergySettledVelocity × ( 1 − TurnDecayCoefficient × ( 1 − cos( TurnAngle ) ) )

search fan-shaped space old branch

Agents from source

Calculate change in velocity from model

Changing pattern as per slope degree

Termination based on water dissipation

Rules guided by Dataset prepared from ML Model

Figure 3.11 combines established hydraulic relationships with project-specific propagation rules. Figure 3.11 Agent propagation rules and Hydraulic geometry and roughness principles provide the basis for relating discharge, channel di- governing equations mensions and flow resistance9,10. Their integration allows each agent to carry a changing hydraulic state rather than following terrain geometry alone. As water is redistributed and progressively dissipated, the algorithm determines whether a path should continue, divide or terminate, producing a channel system responsive to both flow behaviour and topographic conditions.

9 Leopold, L.B. and Maddock, T. Jr. (1953). The Hydraulic Geometry of Stream Channels and Some Physiographic Implications. U.S. Geological Survey Professional Paper 252. 10 Chow, V.T. (1955). ‘A Note on the Manning Formula’. Eos, Transactions American Geophysical Union, 36(4), p. 688.

Figure 3.12 Starting-point selection

Starting points are located within persistent accumulation areas identified under the twenty-year maximum-rainfall scenario, before the water enters paths associated with higher flooding and erosion thresholds. This positions the intervention upstream of the most vulnerable slopes, allowing concentrated runoff to be intercepted at an earlier stage. Each selected point supplies the initial location, flow condition and orientation from which the propagation algorithm begins.

Channel Generation Results

Notes.

Research Development

Initial seeds = 17 Total agent = 741 Growing = 29 Blocked = 90 DIssipated = 520 Merged = 102 Maximum width = 24m Minimum width = 2.1m

LEGEND water channel starting seeds contour line water channel existing river natural flooding path

100

The accumulation areas identified under the twenty-year rainfall scenario establish the starting seeds defined in Figure 3.12. Located upstream of the higher flooding and erosion thresholds, these points allow concentrated runoff to be intercepted before it reaches vulnerable slopes. From these locations, the agents respond to local terrain and changing hydraulic conditions to produce the channel system shown in Figure 3.13. The resulting paths follow contours across steeper ground, extending the flow distance and limiting direct downhill acceleration, before turning towards existing rivers as the terrain flattens. Channel widths vary according to the discharge carried by each branch. Routes terminate when velocity is sufficiently dissipated or when existing drainage is reached; where neither condition provides adequate release, the endpoints become collection points and terrace-retention areas. The final system therefore connects the initial hydrological analysis with differentiated strategies of routing, connection and temporary storage.

Figure 3.13 Resolved channel paths across the Escazú terrain

101

Channel Performance Validation

Accumulation corridor

Natural drainage path Water accumulation and erosion Research Development

Research Development

80% reduction in quantity Seasonal Streams

Altered by channel system Existing River

Retained for channel dispersal

LEGEND height debris river water

102

Re-running the terrain simulation with the proposed channel system demonstrates that runoff is redistributed before it can concentrate along the previous downslope erosion corridors. Water is divided between the branching channels and redirected towards existing rivers, reducing both local accumulation and the capacity of the flow to transport debris. Consequently, the continuous erosion path observed in the pre-intervention scenario no longer develops, while the remaining runoff is dispersed into lower-intensity seasonal flows. The result validates the intervention as a strategy for modifying water concentration, velocity and direction rather than attempting to eliminate runoff from the terrain.

Figure 3.14 Agent propagation rules and governing equations

103

Comparative Analysis of Water and Debris Dissipation

After intervention

Before interventi on

Water dissipation - Water dissipation

Water accumulation Water accumulation Area water

Volume

Area Volume

22 %

Area

12 % 5.20 %

Area

Water dissipation - Water dissipation Volume

water

1.20%

Volume

22 %22 %

Area Area

Water dissipation -

1.20% 1.20%

Volume Volume Water dissipation Debris dissipation -

22 %

Area Debris dissipation Area

22 % 1.20% 24 % 1.20%

Volume

Area Volume Debris dissipation Area DebrisVolume dissipation -

Area debris

Volume

Area Volume

24 % 1.45%

1.45%

Volume

16 % 6.58 %

24 %

Area

Debris dissipation Volume Relative differenceDebris dissipation - -

Research Development

Debris accumulation Debris accumulation -

22 %

24 %

1.45%

Volume debris Relative difference-

76-83 %24 % Water dissipation Area 24 % Area Relative differenceDebris dissipation 1.45% Volume 1.45% 76-83 % Volume Water dissipation50-77%

Relative difference-

Debris dissipation

Water dissipation

50-77%

76-83 %

76-83 % Debris dissipation Water dissipation Relative differenceRelative difference- 50-77% Debris dissipation

50-77%

76-8376-83 % % Water dissipation Water dissipation

Debris dissipation

Debris dissipation50-77% 50-77%

LEGEND water debris

Notes:

The comparative simulation indicates that water and debris are no longer concentrated within narrow downslope corridors. Following the channel intervention, both are dispersed across a larger area but at substantially lower volume and flow energy, producing a thinner and less destructive distribution. This redistribution interrupts the continuous erosion pattern identified in the original simulation and reduces the potential for further material mobilisation. Regional debris-flow hazard is determined not by affected area alone, but through the combined evaluation of flow depth, velocity, volume and event probability11. On this basis, the intervention indicates a transition towards lower hazard intensity. 11 Guo, Y., Feng, Z., Wang, L., Tian, Y. and Chen, L. (2024). ‘Hazard Assessment of Debris Flow: A Case Study of the Huiyazi Debris Flow’. Water, 16(10), 1349.

104

Figure 3.15 Comparative evaluation of channel-system effects on water and debris accumulation

105

Renton, M. and Poot, P. (2014). ‘Simulation of the evolution of root water foraging strategies in dry and shallow soils’, Annals of Botany, 114(4), pp. 763–778.

Mora, S. and Vahrson, W.G. (1994) ‘Macrozonation methodology for landslide hazard determination’, Bulletin of the Association of Engineering Geologists, 31(1), pp. 49–58.

Montgomery, D.R. and Dietrich, W.E. (1994) ‘A physically based model for the topographic control on shallow landsliding’, Water Resources Research, 30(4), pp. 1153–1171.

Ayalew, L., Yamagishi, H. and Ugawa, N. (2004) ‘Landslide susceptibility mapping using GIS-based weighted linear combination: The case in Tsugawa area of Agano River, Niigata Prefecture, Japan’, Landslides, 1(1), pp. 73–81.

Roudier, P., Peroche, B. and Perrin, M. (1993) ‘Landscapes synthesis achieved through erosion and deposition process simulation’, Computer Graphics Forum, 12(3), pp. 375–383.

Wang, C., Li, S. and Esaki, T. (2008) ‘GIS-based two-dimensional numerical simulation of rainfall-induced debris flow’, Natural Hazards and Earth System Sciences, 8, pp. 47–58.

Hsu, Y.-C. and Liu, K.-F. (2019) ‘Combining TRIGRS and DEBRIS-2D models for the simulation of a rainfall infiltration-induced shallow landslide and subsequent debris flow’, Water, 11(5), Article 890.

Han, Y., Li, T., Wang, S. and Chen, J. (2020) ‘A flow-measuring algorithm of arc-bottomed open channels through multiple characteristic sensing points of the flow-velocity sensor in agricultural irrigation areas’, Sensors, 20(16), Article 4504.

Leopold, L.B. and Maddock, T. Jr. (1953). The Hydraulic Geometry of Stream Channels and Some Physiographic Implications. U.S. Geological Survey Professional Paper 252.

10.

Chow, V.T. (1955). ‘A Note on the Manning Formula’. Eos, Transactions American Geophysical Union, 36(4), p. 688.

11.

Guo, Y., Feng, Z., Wang, L., Tian, Y. and Chen, L. (2024). ‘Hazard Assessment of Debris Flow: A Case Study of the Huiyazi Debris Flow’. Water, 16(10), 1349.

CONCLUSION This chapter develops a terrain-responsive strategy for reducing rainfall-induced flooding and slope instability in Escazú. Environmental and hydrological analysis first identifies where runoff, steep terrain and vulnerable soils combine to create intervention priorities. The resulting accumulation patterns establish both the spatial focus of the proposal and the starting points from which a distributed drainage system can develop. CFD testing, regression-based prediction and agent-based propagation translate these conditions into channel geometry. The system adjusts its direction, width and branching behaviour according to terrain and flow performance, allowing runoff to be intercepted, divided, slowed and either retained or reconnected with existing rivers. Comparative simulation indicates that the intervention reduces concentrated water and debris accumulation by distributing both across a wider area at lower volume and energy. The outcome is therefore not a fixed drainage layout, but an adaptive network derived from the interaction between water behaviour and existing topography. Although further calibration and physical testing would be required before implementation, the study demonstrates how computational modelling can guide minimal and strategically located modifications to a hydrologically vulnerable landscape.

106

Research Development

CHAPTER BIBLIOGRAPHY

107

Material Experimentation

MATERIAL EXPERIMENTATION

108

109

Material Experimentation

Material Experimentation The material research explores how rice husk ash (RHA)-based composites can contribute to the design of landscapes exposed to unstable ground and changing water conditions. Rather than treating material development as an isolated technical exercise, the study positions it within the wider environmental strategy of the project, where construction is expected to participate in the regulation of water and the stabilisation of vulnerable terrain. The investigation focuses on developing a material system whose behaviour can be adjusted according to different site conditions. RHA is used as the main component because of its potential as an agricultural waste resource and its capacity to form reactive composites when combined with suitable binders and additives. Through iterative experimentation, the research examines how changes in composition canshift the relationship between the material and water. The aim is not to produce a single optimum mixture, but to establish a range of material responses that can support different environmental conditions across the project. This creates a direct connection between material development and the larger landscape strategy, allowing construction to respond more selectively to moisture, saturation, and changing ground conditions.

110

111

Introduction Hydrophobic and Hydrophilic Strategy

Material Experimentation

Research Aim

The material research investigates two contrasting performance targets within the RHA-based system: hydrophilic and hydrophobic behaviour. These conditions are developed as opposite ends of a material gradient, allowing the composite to be tuned according to different hydrological requirements. Hydrophilic mixtures are designed to absorb, transmit, and retain moisture, supporting controlled water exchange within the material and surrounding soil. In contrast, hydrophobic mixtures are developed to limit water penetration and protect areas where saturation must be reduced. Establishing these contrasting behaviours provides a material basis for regulating water retention and movement while supporting soil stabilisation across changing site conditions1.

112

1 Gautam, and Debjit Bhowmik. “Application of Lime-Rice Husk Ash Mixture (LRHA) for the Stabilization of Fine-Grained Soil: A State-of-the-Art Review.” Innovative Infrastructure Solutions 8 (2023): 308. https://doi.org/10.1007/s41062-023-01269-5.

Figure 4.1 Hydrophilic and hydrophobic material performance targets

Figure 4.2 Hydrophilic- hydrophobic water regulation strategy

The strategy focuses on how hydrophilic and hydrophobic mixtures are spatially combined rather than treated as independent material types. Their distribution creates transitional zones where material behaviour can shift gradually from absorption to resistance according to local water pressure, exposure, and ground condition. This allows components to be layered or compositionally adjusted along their length, producing a material gradient that responds to changing hydrological demands and reduces abrupt differences between drainage, retention, and soil-contact conditions2.

2 Iyyappan, G. R., G. Geetha, and S. K. Divya. “Experimental Investigation on Clay Soil Stabilization Using Lime and Rice Husk Ash.” IJIRST—International Journal for Innovative Research in Science & Technology 2, no. 12 (2016): 158–164.

113

Material Experimentation

Material Selection (RHA) and Development Framework

114

Rice husk ash (RHA) is selected as the primary material because its properties align with the hydro- Figure 4.3 RHA selection logic and sitelogical conditions and stabilisation requirements of the site. Produced from rice-processing waste, responsive development of RHA-based RHA transforms an agricultural residue into a construction resource while reducing dependence on composites more carbon-intensive conventional materials3. Its high content of reactive amorphous silica enables pozzolanic reactions with lime-based binders, improving the strength, cohesion, and water resistance of moisture-sensitive soils. Depending on its processing and binder system, the material can also contribute to carbon reduction and carbonation over its lifecycle. RHA therefore operates beyond a low-impact material alternative, becoming part of a material strategy that connects agricultural waste, water regulation, and soil stabilisation4.

The material development strategy establishes a circular process in which local agricultural waste is transformed into a site-responsive construction resource. Rice husk is processed into RHA, combined with locally available constituents, and progressively refined through physical testing to produce composites with specific hydrological and structural behaviours. This approach connects local material production with the environmental demands of the site, allowing waste resources to be reintegrated into the landscape through construction5,6.

3 Chabannes, Morgan, Eric Garcia-Diaz, Laurent Clerc, and Jean-Charles Bénézet. “Studying the Hardening and Mechanical Performances of Rice Husk and Hemp-Based Building Materials Cured under Natural and Accelerated Carbonation.” Construction and Building Materials 94 (2015): 105–115. https://doi.org/10.1016/j.conbuildmat.2015.06.032.

5 Anjum, Saba, Abhishek Sharma, Nandey C. Olleyowe, Abdullah H. Alsabhan, Shamshad Alam, Kanwarpreet Singh, Aditya Kumar Tiwary, Sahil Sharma, and Jibran Qadri. “Sustainable Subgrade Improvement with Calcium Carbide Residue and Rice Husk Ash.” Scientific Reports 15 (2025): 14351. https://doi.org/10.1038/s41598-025-98833-z. 6 Cook, David J., and P. Suwanvitaya. “Properties and Behaviour of Lime-Rice Husk Ash Cements.” ACI Special Publication 79 (1983): 831–846. https://doi.org/10.14359/6728.

4 Chen, Ruifeng, Surya Sarat Chandra Congress, Guojun Cai, Wei Duan, and Songyu Liu. “Sustainable Utilization of Biomass WasteRice Husk Ash as a New Solidified Material of Soil in Geotechnical Engineering: A Review.” Construction and Building Materials 292 (2021): 123219. https://doi.org/10.1016/j.conbuildmat.2021.123219.

115

Preliminary Physical Experimentation

Material Experimentation

Experimental Materials

The material experiments investigate how RHA can be combined with soil, binders, and reinforcement to form a more stable composite under wet ground conditions. Each constituent is introduced to modify the behaviour of the mixture and improve its performance when exposed to water. This establishes a consistent material base from which hydrophilic and hydrophobic variations can be developed and compared7. Figure 4.4 Material constituents and functional roles in RHA-based composites

116

7 Handayani, Lia, Sri Aprilia, Abdullah, Cut Rahmawati, Teuku Budi Aulia, Péter Ludvig, and Jawad Ahmad. “Sodium Silicate from Rice Husk Ash and Their Effects as Geopolymer Cement.” Polymers 14, no. 14 (2022): 2920. https://doi.org/10.3390/polym14142920.

117

The process is intentionally iterative, with unsuccessful mixtures forming part of the development rather than being discarded from the research. Samples that cracked, dissolved, absorbed excessive water, or lacked sufficient cohesion were used to identify weaknesses in the composition and guide subsequent adjustments. Through repeated testing and refinement, the experiments progressively narrow the range of mixtures toward compositions that provide the most suitable balance between material stability and the required hydrological behaviour8.

Material Experimentation

Material Experiment Process

The experimental process establishes a consistent method for producing and comparing the RHA-based samples. Each mixture is prepared using controlled proportions, cast into standardised specimens, and cured before its initial condition and dry weight are recorded. Maintaining the same basic procedure allows changes in performance to be traced back to differences in composition rather than variations in fabrication.

118

8 Reis, Joice Batista, Giovanna Peisser, William Mateus Kubiaki Levandoski, Suéllen Tonatto Ferrazzo, Jonas Duarte Mota, Adriana Augustin Silveira, and Eduardo Pavan Korf. “Experimental Investigation of Binder Based on Rice Husk Ash and Eggshell Lime on Soil Stabilization under Acidic Attack.” Scientific Reports 12 (2022): 7542. https://doi.org/10.1038/s41598-022-11529-6.

Figure 4.5 RHA material experiment process

119

Stage 2 - Hydrophilic Test

RHA-HB-01 TEST

RHA-HB-02 TEST

RHA-HB-03 TEST

RHA-HF-01B

RHA-HF-01C

RHA-HF-01D

Mix Ratio

13.0%

14.3%

initial water (22.5m in ml)

initial water (25ml) in

30% RHA (52.5g)

17.1%

8.7%

Material Experimentation

Stage 1 - Hydrophobic Test

11.8% 21.7%

initial nitial water (20m nit (20ml) m

22.3%

RHA (37.5g)

35.3% 17.6%

sodium silicate (15g) sodiu sodi g))

23.1%

hydrated lime (30g)

29.6%

26.9%

hydrated lime (30g)

RHA (35g)

RHA (40g)

RHA (60g)

24.0%

hydrated lime (30g)

RHA (30g)

sodium silicate (30g)

26.1% 17.1%

21.4%

hydrated lime (30g)

soil (37.5g)

cement (45g)

22.2% 30.4% soil (52.5g)

8.8% hydrated lime (1 (15g)

The hydrophobic tests evaluate how effectively each RHA-based mixture resists water penetration Figure 4.6 Hydrophobic testing while maintaining its physical integrity. Each cured sample is first weighed dry, then submerged in water for 12 hours and weighed again to determine the change in mass caused by water absorption. The samples are also visually assessed for cracking, softening, erosion, or loss of material. Mixtures with lower water uptake and less physical deterioration are considered to have stronger hydrophobic performance and are selected for improvements9.

120

soil (20g)

26.5% soil (45g)

9 Tuhin, M. T. H., M. M. Hassan, M. M. Hasan, M. S. A. Julfikar, and M. S. M. Farooq. “Stabilization of Soil by Rice Husk Ash.” Paper ID 126, Department of Civil Engineering, Chittagong University of Engineering & Technology, n.d.

14.8%

rock dust (30g)

23.1%

15.4%

rock dust (30g)

soil (20g)

16.0% 24.0%

soil (20g)

rock dust (30g)

11.1%

11.5%

12.0%

china clay (15g))

china clay (15g)

Figure 4.7 Hydrophilic testing

The hydrophilic tests evaluate the capacity of each RHA-based mixture to absorb and retain water while remaining physically stable. Each cured sample is first weighed dry, submerged in water for 12 hours, and weighed again to measure the increase in mass produced by water absorption. Unlike the hydrophobic tests, greater water uptake is desirable, provided that the sample maintains its form without excessive softening or disintegration. The test therefore identifies mixtures that can retain moisture within the material while preserving sufficient cohesion for use in water-retention areas10.

10 Thomas, Blessen Skariah. “Green Concrete Partially Comprised of Rice Husk Ash as a Supplementary Cementitious Material—A Comprehensive Review.” Renewable and Sustainable Energy Reviews 82, pt. 3 (2018): 3913–3923. https://doi.org/10.1016/j. rser.2017.10.081.

121

Stage 3 - Inclusion of Fibers

R2-HB BASLAT-01

R2-HB COIR-01

Material Experimentation

R2-HB BAMBOO-01

Figure 4.9 Failed sample from initial material trials

R2-HB BAMBOO-02

R2-HB BASLAT-02

The fibre inclusion tests investigate whether reinforcement can improve the structural stability, strain factor and flexural coefficient of the selected RHA-based mixture without compromising its hydrophobic performance. Bamboo, basalt, and coir fibres are incorporated into comparable samples and subjected to the same water-exposure procedure used in the previous stage 2. Their performance is assessed through changes in water absorption and visible material deterioration, allowing the effect of each fibre type on cracking and integrity to be compared11,12,13.

122

11 Adesina, Adeyemi. “Performance of Cementitious Composites Reinforced with Chopped Basalt Fibres—An Overview.” Construction and Building Materials 266 (2021): 120970. https://doi.org/10.1016/j.conbuildmat.2020.120970. 12 Jan, Rahila, Mohd. Irshad Malik, and Amanpreet Tangri. “Influence of Lime, Rice Husk Ash and Coconut Fibre on Strength Properties of Subgrade.” International Journal of Innovative Technology and Exploring Engineering 9, no. 8 (2020): 767–772. https://doi. org/10.35940/ijitee.H6721.069820. 13 Wydra, Małgorzata, Piotr Dolny, Grzegorz Sadowski, and Jadwiga Fangrat. “Flexural Behaviour of Cementitious Mortars with the Addition of Basalt Fibres.” Materials 14, no. 6 (2021): 1334. https://doi.org/10.3390/ma14061334.

R2-HB COIR-02

Figure 4.8 Fiber testing

Figure 4.10 Sample failure following three-point tension test

123

Selected Samples for Further Research Selected Baseline Samples

Iterative Material Samples Hydrophobic

RHA-HB-01

RHA-HB-02

RHA-HB-03

RHA-HB-04

Mix Ratio

RHA-HF-01A

2.8% basalt fibers (5g)

24.2%

10.4%

12.1%

17.3%

iniitial water (20ml) 0m

RHA (30g)

RHA (22g)

RHA (40g)

Mix Ratio

11.3% iinitial water (20ml) ml)

17.3%

18.2% sodium silicate (30g)

12.4%

initial nitial water (18 (18ml) 18

sodium silicate (30g)

21.7%

21.2%

17.0%

soil (37.5g)

sodium silicate (30g)

soil (37.5g)

22.7%

20.2%

15.2%

soil (37.5g)

hydrated lime (35g)

hydrated lime (25g))

7.6%

12.7% rock dust (22.5g)

13.0%

22.6% hydrated lime (40g)

rock dust (22.5g)

roc ck dust (12.5 5g) 5

14.3%

21.2%

initial water (25ml) in ini m

Material Experimentation

17.1%

27.3%

initial water (35ml)

RHA (45g)

Hydrophilic

sodium silicate (30g)

15.2%

12.1%

hydrated lime (25g))

17.1%

21.4%

hydrated lime (30g)

soil (37.5g)

Material Experimentation

30% RHA (52.5g)

soil (20g)

6.1% 18.2%

0g) 0g g) g china na clay (10g

rock dust (30g)

LEGEND

RHA-HF-01E

RHA-HF-01F

RHA-HF-01G

Mix Ratio

19.1%

17.2%

RHA (30g)

initial water (26-28ml)

22.3% hydrated lime (35g)

22.3%

rock dust (35g)

124

18.8%

14.7%

3.8%

in nitial water (22-25ml) n ml)

sodium iu u silicate (6g)

RHA (30g)

12.5%

soil (20g)

25.5% 6.4%

hydrated lime (40g)

china clay (10g) (10 (10g

6.4% china clay y (1 (10g)

14 Pushpakumara, B. H. J., and W. S. W. Mendis. “Suitability of Rice Husk Ash (RHA) with Lime as a Soil Stabilizer in Geotechnical Applications.” International Journal of Geo-Engineering 13 (2022): 4. https://doi.org/10.1186/s40703-021-00169-w.

17.2% initial water (26-28ml)

12.7% 12.7% soil (20g)

RHA-HB-01 and RHA-HF-01A were selected as baseline samples because they established the Figure 4.11 Baseline analysis clearest initial contrast between water-resistant and water-absorbing behaviour while maintaining sufficient material cohesion for further development. These samples became reference points for evaluating later changes in composition rather than final mixtures. As shown in the graph, the following iterations progressively adjust the balance between RHA and mineral constituents to shift material behaviour while maintaining a comparable material base. Within the hydrophobic series, this refinement culminates in HB-04, where the composition is further reinforced with basalt fibres to improve structural integrity while preserving its resistance to water penetration14.

15.9% RHA (25g)

Figure 4.12 Samples analysis

22.3% rock dust (35g)

soil (20g)

21.9% hydrated lime (35g)

3.1% china clay ch ay y (5 (5g)

25.1% rock dust (40g)

The RHA-HF series was developed from the hydrophilic baseline to maintain controlled water absorption while addressing the softening and material loss observed during immersion. Each iteration modified the composition to improve stability under saturated conditions without eliminating permeability, while HF-01G introduced a small amount of sodium silicate to further strengthen the material matrix. As shown through the iterative samples, this process gradually shifted the hydrophilic mixtures from highly absorptive but unstable behaviour toward a more controlled balance between water uptake and material integrity15.

125

Water Soaking Test

Compressive Strength Test

5 cm

weight (g)

Initial weight documentation

Test samples

5 cm

counterweight weight (g)

Final weight documentation

12 hrs water soaking

weight (g)

Initial weight documentation

Test samples

Hydrophilic

RHA-HB-02

RHA-HB-03

RHA-HB-04

RHA-HF-01E

RHA-HF-01F

RHA-HF-01G

WInitial = 238g W7dayDry = 208.6g

WInitial = 259g W7dayDry = 201.4g

WInitial = 283g W7dayDry = 220.8g

WInitial = 298g W7dayDry = 202.2g

WInitial = 274g W7dayDry = 170.9g

WInitial = 278g W7dayDry = 168.46g

28.75kg

20kg

30kg +

Material Experimentation

Hydrophobic

12 hrs water soaking

56.12 46.28

32.5 LEGEND 8.3

7.7

4.3

The water soaking test evaluates how each RHA-based mixture responds to prolonged moisture Figure 4.13 Water soaking analysis exposure. After recording the dry weight, samples were fully submerged for 12 hours, allowed to rest for five minutes, and weighed again to determine water uptake. As shown in the graph, the hydrophobic samples remained below 10% absorption, with RHA-HB-02 at 8.30%, RHA-HB-03 at 7.70%, and RHA-HB-04 at 4.30%, identifying HB-04 as the most water-resistant mixture. In contrast, the hydrophilic samples showed substantially higher absorption, increasing from 32.50% in RHA-HF-01E to 46.28% in RHA-HF-01F and 56.12% in RHA-HF-01G. The graph therefore confirms a clear separation between both material behaviours. Alongside weight change, each sample was examined for cracking, deformation, and material loss to verify that the intended hydrological response could be maintained without compromising physical stability15,16.

126

15 Peng, Hsuan-Yi, Kuan-Yu Yeh, Bang-Yan Liu, and Li-Jen Chen. “Defining Hydrophilicity and Hydrophobicity through Advancing Contact Angles and Wetting Transitions.” Journal of Colloid and Interface Science 708 (2026): 139801. https://doi.org/10.1016/j. jcis.2025.139801. 16 Shah, Mudassar Munir, and Hong Li. “Utilization of Lime-Mixed Geopolymerized Rice Husk Ash (LGR) for Balanced Amelioration of Collapsible Soil.” Journal of Rock Mechanics and Geotechnical Engineering 17 (2025): 3925–3949. https://doi.org/10.1016/j. jrmge.2024.12.022.

Figure 4.14 Structural testing - compression test

The compressive load test assessed whether the developed mixtures could maintain structural integrity after prolonged water exposure. Following the 12-hour soaking test, loads were applied incrementally using 1.25, 2.5, 5, and 10 kg discs until failure or the maximum test capacity was reached. Within the hydrophobic series, RHA-HB-02 supported approximately 28.3 kg, RHA-HB-03 reached 20 kg, and RHA-HB-04 exceeded 30 kg, confirming HB-04 as the strongest hydrophobic iteration. The hydrophilic samples RHA-HF-01E, RHA-HF-01F, and RHA-HF-01G also exceeded 30 kg despite their substantially higher water absorption and lower dry weights. The results show that increasing hydrophilic behaviour did not necessarily compromise load-bearing performance. The hydrophobic series achieved reported compressive-strength values of approximately 0.65–0.69 MPa, approaching the performance range required for low-strength earthen components, while the hydrophilic series demonstrated greater resistance to compression17.

Notes: WInitial = Initial weight, W7daydry = Weight after 7 days of dry curing 17 Manaviparast, Hamid Reza, Nuno Cristelo, Eduardo Pereira, and Tiago Miranda. “A Comprehensive Review on Clay Soil Stabilization Using Rice Husk Ash and Lime Sludge.” Applied Sciences 15, no. 5 (2025): 2376. https://doi.org/10.3390/app15052376.

127

Tension Test

Comparative Analysis counterweight

5 cm 5 cm

weight (g)

brick

Initial weight documentation

Test samples

Hydrophobic

RHA-HF-01E

Tensile loading

RHA-HF-01F

RHA-HF-01G

Hydrophilic RHA-HB-04 WInitial: 345 g

WInitial: 375 g

W7dayDry: 183.5 g

W7dayDry: 205.1 g

RHA-HB-03

RHA-HB-04

(without fibers)

(with fibers)

①

②

③

①

②

③

④

Initial state

657.5g

25g + 1.25kg = 1.275kg

Initial state

25g + 1.25kg

25g + 2.5kg

657.5g + 50g + 2*1.25kg = 3.2075kg

Material Experimentation

RHA-HB-03

Figure 4.16 Hydrophobic and hydrophilic selected samples

25g + 2.5kg = 2.525kg

657.5g + 50g + 2*2.5kg = 5.7075kg

The tension test assessed the samples through three-point bending, with each specimen support- Figure 4.15 Structural testing ed at both ends and progressively loaded at the centre until failure. RHA-HB-03 showed limited three - point test resistance without reinforcement, while the fibre-reinforced RHA-HB-04 sustained substantially higher loading, demonstrating the contribution of basalt fibres to flexural performance and material integrity. The strongest specimens reached an estimated flexural strength of approximately 0.9 MPa, establishing a performance threshold that can inform the design of components subjected to greater tensile stresses. The results therefore support the use of fibre reinforcement where the material must resist bending rather than compression alone18.

128

Notes: WInitial = Initial weight, W7daydry = Weight after 7 days of dry curing 18 Owino, Alex Otieno, Najmun Nahar, Zakaria Hossain, and Noma Tamaki. “Effects of Basalt Fibres on Strength and Permeability of Rice Husk Ash-Treated Expansive Soils.” Journal of Agricultural Engineering 53 (2022): 1315. https://doi.org/10.4081/jae.2022.1315.

The comparative analysis shows how changes in composition progressively shifted the hydrophobic mixtures toward lower water absorption and improved structural performance. Reducing the proportion of RHA while increasing hydrated lime and mineral content corresponded with a decrease in absorption from 8.30% in HB-02 to 4.30% in HB-04. All three mixtures showed a similar estimated Young’s modulus, ranging from 0.6540 to 0.6928 MPa, while HB-04 reached the maximum applied compressive load of 30 kg and sustained 3.21 kg under flexural loading, more than twice the resistance recorded for HB-03. These combined results identify HB-04 as the most developed hydrophobic formulation, balancing low water penetration with sufficient stiffness and improved flexural integrity for its application within drainage channels. The hydrophilic series demonstrates a different relationship between water absorption and mechanical behaviour. Absorption increased from 32.50% in HF-01E to 46.28% in HF-01F and 56.12% in HF-01G, while all three samples reached the maximum applied compressive load of 30 kg. Their estimated Young’s modulus varied more substantially, from 0.5886 MPa in HF-01F to 1.1772 MPa in HF-01E, with HF-01G reaching 0.6540 MPa, showing that increased water absorption does not produce a uniform reduction in stiffness. Rather than defining a single optimum mixture, the comparison establishes a tunable range of material behaviour in which hydrological response and mechanical performance can be adjusted according to the requirements of each intervention18. 18 Owino, Alex Otieno, Najmun Nahar, Zakaria Hossain, and Noma Tamaki. “Effects of Basalt Fibres on Strength and Permeability of Rice Husk Ash-Treated Expansive Soils.” Journal of Agricultural Engineering 53 (2022): 1315. https://doi.org/10.4081/jae.2022.1315.

129

CONCLUSION

Design Development

Material Experimentation

The material experiments demonstrate that RHA-based composites can be tuned toward distinct hydrological and mechanical responses rather than developed as a single fixed material. Through iterative testing, unstable early mixtures were progressively refined, with HB-04 showing the most consistent combination of low water uptake and structural resistance, while the hydrophilic series achieved substantially greater absorption without an equivalent loss of compressive capacity. The study therefore establishes material composition as a variable that can be adjusted in relation to different hydrological demands across the site. The main limitation is that these results remain comparative rather than fully validated material properties. The distinction between hydrophobic and hydrophilic behaviour is currently inferred primarily from water uptake, while the mechanical results are based on small specimens and simplified loading conditions. The experiments therefore demonstrate performance tendencies, but not yet the long-term behaviour of the material at construction scale. Further development should focus on validating these tendencies through standardized hydrological and mechanical testing, followed by larger-scale prototypes in which the mixtures can be evaluated under conditions closer to those of the site. This next stage is necessary to determine whether the material gradient can operate reliably as a construction system rather than only as an experimental material framework.The material experiments demonstrate that RHA-based composites can be tuned toward distinct hydrological and mechanical responses rather than developed as a single fixed material. Through iterative testing, unstable early mixtures were progressively refined, with HB-04 showing the most consistent combination of low water uptake and structural resistance, while the hydrophilic series achieved substantially greater absorption without an equivalent loss of compressive capacity. The study therefore establishes material composition as a variable that can be adjusted in relation to different hydrological demands across the site.

130

131

132

Gautam, and Debjit Bhowmik. “Application of Lime-Rice Husk Ash Mixture (LRHA) for the Stabilization of Fine-Grained Soil: A State-of-the-Art Review.” Innovative Infrastructure Solutions 8 (2023): 308. https://doi.org/10.1007/s41062-02301269-5.

16.

Shah, Mudassar Munir, and Hong Li. “Utilization of Lime-Mixed Geopolymerized Rice Husk Ash (LGR) for Balanced Amelioration of Collapsible Soil.” Journal of Rock Mechanics and Geotechnical Engineering 17 (2025): 3925–3949. https://doi. org/10.1016/j.jrmge.2024.12.022.

Iyyappan, G. R., G. Geetha, and S. K. Divya. “Experimental Investigation on Clay Soil Stabilization Using Lime and Rice Husk Ash.” IJIRST—International Journal for Innovative Research in Science & Technology 2, no. 12 (2016): 158–164.

17.

Chabannes, Morgan, Eric Garcia-Diaz, Laurent Clerc, and Jean-Charles Bénézet. “Studying the Hardening and Mechanical Performances of Rice Husk and Hemp-Based Building Materials Cured under Natural and Accelerated Carbonation.” Construction and Building Materials 94 (2015): 105–115. https://doi.org/10.1016/j.conbuildmat.2015.06.032.

Manaviparast, Hamid Reza, Nuno Cristelo, Eduardo Pereira, and Tiago Miranda. “A Comprehensive Review on Clay Soil Stabilization Using Rice Husk Ash and Lime Sludge.” Applied Sciences 15, no. 5 (2025): 2376. https://doi.org/10.3390/ app15052376.

18.

Chen, Ruifeng, Surya Sarat Chandra Congress, Guojun Cai, Wei Duan, and Songyu Liu. “Sustainable Utilization of Biomass Waste-Rice Husk Ash as a New Solidified Material of Soil in Geotechnical Engineering: A Review.” Construction and Building Materials 292 (2021): 123219. https://doi.org/10.1016/j.conbuildmat.2021.123219.

Owino, Alex Otieno, Najmun Nahar, Zakaria Hossain, and Noma Tamaki. “Effects of Basalt Fibres on Strength and Permeability of Rice Husk Ash-Treated Expansive Soils.” Journal of Agricultural Engineering 53 (2022): 1315. https://doi. org/10.4081/jae.2022.1315.

Anjum, Saba, Abhishek Sharma, Nandey C. Olleyowe, Abdullah H. Alsabhan, Shamshad Alam, Kanwarpreet Singh, Aditya Kumar Tiwary, Sahil Sharma, and Jibran Qadri. “Sustainable Subgrade Improvement with Calcium Carbide Residue and Rice Husk Ash.” Scientific Reports 15 (2025): 14351. https://doi.org/10.1038/s41598-025-98833-z.

Cook, David J., and P. Suwanvitaya. “Properties and Behaviour of Lime-Rice Husk Ash Cements.” ACI Special Publication 79 (1983): 831–846. https://doi.org/10.14359/6728.

Handayani, Lia, Sri Aprilia, Abdullah, Cut Rahmawati, Teuku Budi Aulia, Péter Ludvig, and Jawad Ahmad. “Sodium Silicate from Rice Husk Ash and Their Effects as Geopolymer Cement.” Polymers 14, no. 14 (2022): 2920. https://doi.org/10.3390/ polym14142920.

Reis, Joice Batista, Giovanna Peisser, William Mateus Kubiaki Levandoski, Suéllen Tonatto Ferrazzo, Jonas Duarte Mota, Adriana Augustin Silveira, and Eduardo Pavan Korf. “Experimental Investigation of Binder Based on Rice Husk Ash and Eggshell Lime on Soil Stabilization under Acidic Attack.” Scientific Reports 12 (2022): 7542. https://doi.org/10.1038/s41598022-11529-6.

Tuhin, M. T. H., M. M. Hassan, M. M. Hasan, M. S. A. Julfikar, and M. S. M. Farooq. “Stabilization of Soil by Rice Husk Ash.” Paper ID 126, Department of Civil Engineering, Chittagong University of Engineering & Technology, n.d.

10.

Thomas, Blessen Skariah. “Green Concrete Partially Comprised of Rice Husk Ash as a Supplementary Cementitious Material—A Comprehensive Review.” Renewable and Sustainable Energy Reviews 82, pt. 3 (2018): 3913–3923. https://doi. org/10.1016/j.rser.2017.10.081.

11.

Adesina, Adeyemi. “Performance of Cementitious Composites Reinforced with Chopped Basalt Fibres—An Overview.” Construction and Building Materials 266 (2021): 120970. https://doi.org/10.1016/j.conbuildmat.2020.120970.

12.

Jan, Rahila, Mohd. Irshad Malik, and Amanpreet Tangri. “Influence of Lime, Rice Husk Ash and Coconut Fibre on Strength Properties of Subgrade.” International Journal of Innovative Technology and Exploring Engineering 9, no. 8 (2020): 767– 772. https://doi.org/10.35940/ijitee.H6721.069820.

13.

Wydra, Małgorzata, Piotr Dolny, Grzegorz Sadowski, and Jadwiga Fangrat. “Flexural Behaviour of Cementitious Mortars with the Addition of Basalt Fibres.” Materials 14, no. 6 (2021): 1334. https://doi.org/10.3390/ma14061334.

14.

Pushpakumara, B. H. J., and W. S. W. Mendis. “Suitability of Rice Husk Ash (RHA) with Lime as a Soil Stabilizer in Geotechnical Applications.” International Journal of Geo-Engineering 13 (2022): 4. https://doi.org/10.1186/s40703-021-00169-w.

15.

Peng, Hsuan-Yi, Kuan-Yu Yeh, Bang-Yan Liu, and Li-Jen Chen. “Defining Hydrophilicity and Hydrophobicity through Advancing Contact Angles and Wetting Transitions.” Journal of Colloid and Interface Science 708 (2026): 139801. https://doi. org/10.1016/j.jcis.2025.139801.

Material Experimentation

CHAPTER BIBLIOGRAPHY

133

Design Development

134

Design Development

DESIGN DEVELOPMENT

135

Design Development

DESIGN DEVELOPMENT Building on the material development established in the previous chapter, this phase transitions from material theory to physical implementation. It outlines comprehensive build framework on site. The study begins with materialising the precise fabrication of the channel sections and details of on-site construction. With the core water infrastructure generated, it explores strategic water management points to guide the spatial distribution of plantation and massing across the landscape. To establish a highly responsive masterplan, the distribution is heavily informed by crucial underlying environmental factors. Specifically, it targets the utilisation of water combining with local soil stability metrics to determine ecological functions and program mixes that can seamlessly intersect the surrounding terrain. A gradient workflow is finally deployed to facilitate soft boundaries, enabling fluid transitions between natural remediation, agro-production and built structures. Ultimately, it demonstrates how the foundational material logic scales up into a cohesive, ecologically integrated and highly functional architectural ecosystem.

136

137

Water Channel Materialisation

Design Development

Water channel and hydrological distribution

The developed adaptive material can now precisely regulating water behaviour across the landscape. It responds to the regional water requirements based on demand. Plantation areas are exposed to the material typology that behaves as a reservoir. It hold water and slowly releases it into the soil. In the distribution channels, however, the material considered is hydrophobic. It facilitates rapid flow, without percolation, preventing any seepage and saturation into surrounding earth. This keeps the water contained and moves it to desired storage nodes. 138

Figure 5.1 Water distribution requirements in channels and terraces

139

Design Development

Channel Construction Logic

Construction follows a moving-factory logic that progresses incrementally along the drainage network. The channel path is first set out from the branching geometry and excavated to the required profile, after which reinforced anchor sections are cast in situ at defined intervals to secure the system to the ground. Between these anchors, curved RHA hydrophobic components are fabricated with moulds, adapted to the local curvature of each channel segment, and then positioned and connected on site through their joints and sidewalls. This sequence of excavation, anchoring, component fabrication, assembly, and repetition allows the construction process to move along the branching network while maintaining continuity and adapting to changes in terrain and geometry. 140

Figure 5.2 Water channel construction sequence

141

Design Development

Tessellation Aggregation

The spacing of anchors and number of modules between them can vary according to local soil conditions, with anchor intervals ranging approximately from 50–100 m. This creates an adaptable system in which fixed structural points provide stability while the sections respond to the changing geometry of the landscape. The casted blocks due to its size, weight and stress capacity needs to be developed into a modular system, to be able to managed at an human scale.

Figure 5.3 Casting anchors and modular components (top)

These individual tesselations can also be constructed with a varying material framework based on its indivual structural requirements. Figure 5.4 Section tesselation aggregation

142

143

Finite Element Analysis (FEA)

Design Development

Tessellation Details

To manage the vast scale and varying dimention of these water channels, the modular components were generated. A basic U and V division was intentionally kept to prioritise the ease of contruction and to create a fundamental understanding of the different variations in the tessellated parts. This tessellation logic critically respond to localising the structural forces. It is observed that the tension concentrates towards the edges, while compression dominates the central components. Each part now can be independently composed of varying material to fit the requirement. 144

Figure 5.5 Section tessellation and materialisation logic

Figure 5.6 Uniform U and V divisions (top) Non-uniform tessellated components (bottom)

To optimise the geometry, FEA was conducted across multiple variations of U and V division. Through the results, a highly successful model was identified that exhibit minimum displacement. The optimal iterations successfully balanced the internal forces, significantly reducing extreme tension and avoiding failure in compression. In this model, the geometry adapts directly to loads, where individual components become deliberately narrower towards the channel edges. This is a result of the stress produced in each junction along with the orientation of the components. The strategic sizing thus ensures efficient structural efficiency. 145

Fabrication of Tessellated Components

Design Development

Comparative Analysis

Materialising the channel tessellation, the component adapts by varying in thickness and fiber content to manage water pressure and flexure stress. Components thickness along different crosssection is directly dictated by the local water pressure exerted on the channel during maximum precipitation scenario. However, within each cross-section, the material's composition changes. By increasing the fibre content in the peripheral components, the tensile strength is enhanced, effectively resisting the higher flexural stress and tension concentrated in the edges.

146

Figure 5.7 Cross section thickness and module material composition

Figure 5.8 Fabricated prototype model

For the physical fabrication, six channel components were produced at 1:50 scale using the selected RHA-HB-04 mixture. The initial channel surface was divided through a 3 × 6 U–V tessellation, after which the seam locations were adjusted according to the Finite Element Analysis to improve force distribution and reduce critical stress concentrations. The six components—HB-04A, HB-04B and HB-04C, each produced twice—required a combined 4098 cm³ of material, with quantities adjusted according to the volume of each piece. Basalt fibre content was deliberately varied between 30 and 36 g per component, providing additional reinforcement compared with the base mixture to improve tensile performance across the tessellated channel and particularly around the joints and seams.

147

Research Development

Phase II Development

Figure 5.10 Phase II development logic structure-2

In this Development Phase, the spatial distribution of agriculture, bioremediation and urban mass- Figure 5.9 Phase II development logic ing is developed in a more localised scale. It is examined more closely how agriculture production structure-1 and ecological restoration combine, specifically with water channels and collection nodes. To design a harmonious mix between the different ecological systems and occupation zones, this framework allows to create a resilient backbone for urban development layers within it.

148

149

Three Intervention Zones

②

1 km

Research Development

1 km

Research Development

Contains landslide bodies of soil

③

③ ①

Possible avalanche due to river

① Steep zones

Figure 5.11 Three selected intervention zones and site-specific conditions

150

Figure 5.12 Location of three intervention zones within the site selection map

Having established the channel network and fabrication technique, the study now proceeds to develop the use of water and functions around them. Three sites are examined at closer scale to test how the proposed system adapts to contrasting territorial conditions. Each presents different environmental and settlement conditions, allowing the research to compare how the morphology responds. As shown in Figure 5.9, the intervention is not repeated as a fixed form: its density, branching pattern, terrace depth, and construction capacity vary with the pressures of each site. The three zones differ in topography and demand — one contains loose, deeply weathered soil bodies, while another is prone to debris flows owing to the presence of rivers. Steeper, more exposed areas require tighter stabilisation networks and stronger runoff control, while vegetated or partially occupied zones support more distributed channels, planting systems, and gradual development. The relationship between the three demonstrates how a shared design logic can produce distinct spatial outcomes without losing its structural and hydrological coherence. 151

Three Intervention Zones

Research Development

①

Steep Slope Massing

Gentle Slope Agro-Production

Bio-remediation

Zone 1 is selected from the previously identified smaller regions to start simulating our Figure 5.13 Selected region and channels development framework. This area has high slope variations, offering a dynamic terrain to explore (top left) varying strategies for bioremediation, agricultural production and architectural massing. Across Slope fradient and contours (top right) the selected site, the program composition shifts depending how these function components are spatially distributed.

152

Figure 5.14 Small scale areas with different program mix

153

Topological Network and Program Mix

Functional Planning and Spatial Allocation

notes. Environm ental fa ctors

1 precipitation 2 solar radiation 3 fault lines 4 landslide 5 geological aquifer 6 wind 7 road/slope 8 urban occupancy 9 channel 10 erosion

A Agro-production F Bio-remidiation forest R Residence C Social-community recreation I Institution U Utility M Mobility E Research and production W Water collection nodes

2% M

1% W

35% F

39% A

LEGEND LEGEND Functional partitions

Environmental factors

Design Development

M U I

Environmental factors connection

2% C

3% E I 2%

14% R

Primary functional connection

LEGEND LEGEND

2% U

Potential functional connection

| Water collection nodes Functional partitions

| Agro-production factors | Environmental Utility

| Residence Environmental factors connection

| Institution

Primary functional connection

| Research and Production Potential functional connection

LEGEND Functional partitions

Environmental factors

Environmental factors connection

| Institution

Primary functional connection

| Research and Production

Potential functional connection

Before defining the spatial layout, a topological network maps the relationships between the main Figure 5.15 Topological relationship network programme functions and the environmental conditions influencing them. Functional nodes, in- model cluding key programmatic areas such as production, habitation and infrastructure, are connected according to their required level of interaction, while environmental factors such as site conditions and ecological processes influence their relative importance and proximity. This establishes a relational hierarchy that identifies which programmes should remain closely connected, which can operate through secondary relationships, and which are most strongly conditioned by environmental processes, providing the organisational logic for the subsequent spatial distribution of the project. 154

| Residence

Figure 5.16 Program mix diagram for selected site

| Social-Community Recreation Functional planning begins from the settlement growth sequence, moving from water infrastrucBio-Remediation Forest urban octure and productive landscapes toward community functions and |more consolidated cupation. The spatial composition is adjusted according to the environmental conditions of the | Mobility target site, where high instability requires greater emphasis on ground reinforcement, terracing, bioremediation, and agricultural systems before increasing built density. Consequently, agro-production occupies 39% of the site and bioremediation forest 35%, together forming 74% of the total allocation, while residence accounts for 14% and the remaining programmes are distributed in smaller proportions. This allocation establishes landscape stabilisation and production as the primary structure from which settlement can progressively develop. This benchmark distribution was set from the local land-use regulations also keeping in mind the zonal attributes. It is subjected to change based on the terrain and surrounding conditions of the subsequent zones.

155

Topological Network and program mix

Design Development

Water channel

Cellular Automata (CA) Distribution - Final Result LEGEND

Neigbourhood based score

Environemental score

Bidding based replacement

| Permanent Death=Eliminate

| 742 cells (7.4 %)

| Initial state

| 140 cells (1.4 %)

| Water collection nodes

| 18 cells (0.2 %)

| Agro-production

| 4065 cells (40.7 %)

| Utility

| 184 cells (1.8 %)

| Residence

| 1386 cells (13.9 %)

| Institution

| 184 cells (1.8 %)

| Research and Production

| 84 cells (0.8 %)

| Social-Community Recreation | 231 cells (2.3 %) | Bio-Remediation Forest

| 2782 cells (27.8 %)

| Mobility

| 184 cells (1.8 %)

Water collection nodes Water channel

The Cellular Automata distribution translates the functional allocation into a spatial pattern while Figure 5.17 CA simulation rules setup- neighresponding to the site’s environmental constraints and hydrological network. The distribution of bourhood logic, environmental parameters the cell functions is based on the relationship between itself and its neighbour and overlaid envi- and bidding rule ronmental conditions that affect the function. Specific rules limit the maximum number of neighbouring cells assigned to the same function, preventing large continuous zones and instead generating smaller island-like functional clusters distributed across the territory. A soft-boundary logic is also introduced so that programmes can gradually overlap and transition into adjacent functions rather than forming rigid edges. This produces a more integrated landscape where agro-production, bioremediation, residence, community programmes, and infrastructure remain spatially connected while adapting to water channels, collection nodes, and areas excluded from development. 156

Figure 5.18 CA simulation results

157

Bioremediation and Forestry

( iii ) Controlled natural flow with some cascaded areas

( ii ) Geocell and soilnailing for mechanical soil stabilisation.

( iv ) Protective bamboo and sugarcane hedges

Design Development

( i ) Local evergreen plants and bamboo, for soil stabilisation through rooting system

Key map for bioremediation spaces

Within the designated bioremediation zone, soil stabilisation is the crucial goal. It is achieved through ahybrid biological and mechnical framework. Evergreen plantation, bamboo and rhizome mats secure the terrain, while geocells and soil nailing reinforces severely steep areas. Rather than entirely supressing natural forces, the design preserves specific corridors to accomodate controlled erosion and extreme water flows. This is done through considering dynamic natural movements and natural cascading. Dense boundary hedges of bamboo and sugarcane act as a resilient buffers to decelerate and manage runoff. Collectively, this integrated system collectively stabilises highly unstable , erosion prone land while maintaining vital ecological dynamics 158

Figure 5.19 Bioremediation zone (left) Figure 5.20 Bioremediation and forestry interventions (top)

159

Design Development

Agro - Production Distribution

Suitable area distribution Planting distribution by terrain

bamboo

banana

lemon grass

coffee

sugarcane

Key plan for agricultural distribution

The agricultural distribution framework utilises spatial heat maps to allocate five resilient local species that also have good return revenue. Coffee plantation serves as the primary economic crop, prioritised to fit on terraces, exhibit moderate slopes and stable ground conditions. The remaining four species act as a complentary stabilisation system tailored to distinct topographic vulnerabilities. Bamboo is strategically introduces to reinforce high-degree slopes, while limegrass is allocated zones with loose soil compositions. Banana plants, being excellent moisture absorber, are clustered adjacent to water collection and storage points. Finally, sugarcane forms protective barrier hedges along the edges of steep slopes and vulnerable valleys. Together, this data-driven distribution ensures the economic viability of generating a terraced plantations while ecologically securing the broader landscape against erosion and degradation. 160

Figure 5.21 Agro-production zone (left) Figure 5.22 Five selected species distributionSection (top), Plan (bottom)

161

Terrace Variations

slope 1:2

Design Development

slope 1:10

Terrace distribution framework

The terracing system is generated according to slope degree, allowing bench width, depth, and frequency to change as the terrain becomes steeper or flatter. On gentler slopes, wider terraces can be introduced at larger intervals, while steeper gradients require narrower and more frequent cuts to reduce the effective slope length and create more stable stepped profiles. This produces a variable terracing strategy that follows the existing topography rather than applying a uniform section, allowing each terrace sequence to respond directly to local ground conditions while supporting soil retention and productive planting. 162

Equal contours

Terracing patters based on slope degree

Terrace formation Figure 5.23 Terrace on selected site (left) Figure 5.24 Terrace formation governed by slope degree (top)

163

Channel Variations Hydrological variation

high water quantity

A’

low water quantity

Meander in terrace

Cascading

Storage nodes

Absorption buffer

Key Plan – 1x1 km

Design Development

Upper steep slope

1 2

3 4

Lower steep slope

While the established terracing system is fragmented, water management is maintained through a continuous, strategically designed channel framework. To navigate the channels through the terraces, the channels and water infrastructure operates through four distinct localised typologies. First, meandering pathways guide water horizontally along the contours of the terraces. Second, where these terraces break, the system employs cascading drops to seamlessly transfer water to lower elevations. Third, integrated storage nodes collect and retain seasonal runoff, ensuring an adequate water supply during drought periods. Finally, absorption buffers are placed to allow excess water to spread and slowly dissipate into the surrounding landscape. By detailing these four functional behaviors, the subsequent section illustrates exactly how the hydrological network is constructed over irregular terrain. 164

Figure 5.25 Hydrological channel typologies and their integration across terraced terrain

165

Spatial Gradients for Functions

Design Development

Differential Growth Field and Distribution

Bioremediation edge

Plantation mix

Bioremediation & agriculture Less porous

Transient function structures Bigger mass

More porous

Resilient function structures

Combining the various layers of infrastructure together, the study establishes a framework to combine and generate the landscpae with its distinct categories. Meandering and cascading channels, fragmented terracing variations, the forestry and agricultural zones sit on top of the other along with built functions. Based on the final Cellular Automata distribution, vector fields are generated from the topological relationships between neighbouring functions, defining the direction in which each programme can expand and interact. Differential growth is then applied to these fields, allowing the initial discrete cellular clusters to deform, connect, and develop softer spatial boundaries. The resulting geometry is subsequently fitted to the site’s geographic conditions, including the water network and terrain constraints, transforming the abstract computational distribution into a continuous spatial map that more closely responds to the real landscape. 166

Figure 5.26 Functional spatial gradients through differential growth

167

CONCLUSION

Design Development

168

Design Development

This chapter explore the channel fabrication to spatial mapping of functions and plantation on site, along with the water network. The current channel tessellation uses a simple UV framework, prioritising the component distribution and composition logic, leaving room for development of interlocking mechanism. The research then narrows to a localised site exhibiting a diverse mix of agroproduction, bioremediation, and massing. This allows to detail out the distrubution based on site conditions, balancing ecology and occupation. Within a specific zone, it is seen that the functions distribute based on primary water demand and environmental conditions along with neighbourhood relations. Some functions cluster in bigger groups, while more social functions spread out into pockets. This was intended to maintain a balance of proximity between different programs. Bioremediation and agroproduction interventions are subsequently mapped, establishing coffee as the primary economic crop supported by secondary stabilising plantations. Finally, the integration of these agricultural zones, the terracing system and the hydrological channel networks is synthesized through the application of vector fields. This produces a continuous spatial logic across the site, establishing a fluid, soft programmatic gradient that seamlessly respond to the underlying terrain and channel infrastructure.

169

Design Proposal

DESIGN PROPOSAL

170

171

DESIGN PROPOSAL

Design Proposal

172

Design Proposal

The design proposal translates the research methodology into an adaptive agro-urban landscape structured by hydrological behaviour, ground stability, and ecological performance. Rather than imposing a fixed masterplan, the project organises a coordinated landscape system that responds to changing site conditions. Water management establishes the primary spatial framework, guiding areas of stabilisation, productive landscape, and occupation, while architectural interventions vary according to their proximity to unstable ground and natural drainage corridors. Through phased development, temporary and permanent structures are introduced only as environmental conditions become suitable for occupation, allowing the settlement to evolve with the landscape rather than against it. The proposal therefore operates as a spatial and material strategy for transforming landslide-prone terrain into a landscape capable of supporting long-term occupation and adaptation.

173

Research Development

Phase III Development

In this phase urban massing and morphologies are developed on the foundation gradient of the Figure 6.1 Phase III development logic distributed spatial map. Attributed between a range of temporary and permanent structure, that structure-1 rely on function and construction, they form a hierarchy of morphological variation that respond to the dynamic environment. Thus developing an adaptive community, preparing a base for living with the landslides.

174

Figure 6.2 Phase III development logic structure-2

175

Architectural Development

Design Proposal

Master Plan

Figure 6.3 Master plan

176

The master plan consolidates the design strategy at the scale of the wider site, organising development according to hydrological behaviour and degrees of ground stability. The existing flow corridor is maintained as a predominantly ecological zone, allowing water and sediment movement to occur while reducing pressure for occupation in the most vulnerable areas. From this corridor, the landscape is progressively structured through water management and productive cultivation, creating a transition toward more stable zones where settlement can intensify. Architectural programmes are distributed as clustered conditions rather than a continuous urban fabric, responding to the terrain and forming soft boundaries with agricultural and bioremediation areas. These transitional edges operate as ecotones, where ecological, productive, and inhabited conditions overlap rather than remain spatially isolated. The resulting plan establishes a gradual relationship between landscape systems and occupation, allowing urban growth to respond to the environmental capacity of the site.

177

Program zones

Cluster formation

Porosity and size variation

Smoothen clusters

Design Proposal

Function Transitions

Figure 6.4 Function transitions

178

Building on the differential distribution, the spatial organisation is progressively refined into clearer patterns of occupation. The continuous field generated in the previous stage becomes the basis for defining broader programme zones, which are then reorganised into clusters and gradually adjusted to soften their boundaries. This process translates the abstract distribution logic into a more spatially legible arrangement while retaining the gradients established earlier. Rather than fixing each function as a separate zone, the transition process preserves overlap and continuity across the site. Within the design development, this creates a direct link between differential growth and the emerging master plan, strengthening the ecotonal relationship between architecture and landscape.

179

Temporary to Permanent Structures The function distribution establishes the initial spatial relationships of the proposal, defining where different forms of occupation can emerge and how they transition across the site. From this framework, the project develops occupation as a gradual process in which temporary structures establish an initial, low-impact presence before more permanent forms are introduced. As these occupations consolidate, individual nodes begin to cluster and define shared voids, allowing spatial organisation to emerge incrementally rather than through a fixed layout. These formations then inform where more resilient structures can be established, while open areas are retained as part of the wider landscape system. In this way, the transition from temporary to permanent becomes a direct extension of the distribution logic, translating spatial relationships into a phased process of settlement consolidation.

emerging clusters

void structuring

spatial distribution system

Design Proposal

temporary

Figure 6.5 Temporary to permanent structures

180

181

Form Finding

Design Proposal

Building from temporary and permanent occupation, the architectural catalogue translates these stages into distinct material and structural systems. Temporary structures are developed as lightweight bamboo canopies, allowing rapid assembly, disassembly, and adaptation as occupation shifts over time. As settlement consolidates, these are complemented by permanent structures in concrete and stone, providing greater mass and resistance in areas exposed to terrace pressures and potential landslide forces. The resulting morphologies are therefore defined not only by programme, but by the degree of permanence and structural performance required across the site. Figure 6.6 Permanent & lightstructures catalogue-1

182

183

Form Finding

Design Proposal

Residence 1

Utility 1

Structural optimisation is used to refine these morphologies rather than treating their form as Figure 6.7 Permanent & lightstructures predetermined. Ameba informs the permanent structures by redistributing material according catalogue-2 to structural demand and lateral loading, while Graphic Statics guides the geometry of the lightweight systems through force-based relationships. This allows structural behaviour to become an active part of the form-finding process, producing distinct architectural responses within a coherent design system. 184

185

Design Proposal

Figure 6.8 Community development section

Once the structural systems and morphologies are defined, the proposal shifts from individual buildings to how they collectively support habitation across the slope. Settlement is organised around more stable terraces and shared productive spaces, allowing community life to develop without occupying the areas that must remain available for water and ground movement. This creates a spatial structure in which architecture and landscape work together to accommodate environmental change rather than resist it completely. Community growth therefore becomes part of the project’s broader strategy for living with landslides, concentrating occupation where greater permanence is possible while preserving flexibility in more vulnerable areas. 186

187

Design Proposal

189 188

Design Proposal

191 190

Discussion

192

Discussion

DISCUSSION

193

Discussion Hydrological & Territorial Framework

Computational Experiments

Material Experiments

Architectural & Socio Ecological Implications

The research demonstrates that landslide risk in the Calle Lajas watershed cannot be addressed through isolated structural interventions alone. The project instead interprets instability as the outcome of interactions between topography, water movement, vegetation, erosion, and occupation. This shifts the role of architectural design from protecting individual objects toward reorganising the environmental processes that determine where and how occupation can occur.

The computational workflows demonstrate the value of treating design as an iterative system rather than as a sequence leading toward a single final form. Branching, cellular automata, vector diffusion, differential distribution, and structural optimisation operate at different scales, but collectively establish relationships between environmental behaviour and spatial organisation. Their significance lies less in the individual algorithms than in their capacity to maintain feedback between landscape performance and architectural development.

The material investigations extend the territorial strategy into the physical performance of the proposed infrastructure. Rather than establishing a single definitive RHA composite, the experiments demonstrate the possibility of tuning the material toward different hydrological behaviours. Hydrophobic formulations suggest potential applications where water needs to be conveyed, while more absorptive mixtures indicate possible roles in moisture-retaining and vegetated conditions. At this stage, however, these behaviours should be understood as preliminary material tendencies rather than validated construction specifications.

The proposal demonstrates that landslide-prone territories can support occupation when urban development is conceived as an extension of landscape management rather than as a separate layer imposed on it. Agroforestry is particularly relevant because it allows productive land to participate in the gradual transformation of unstable ground, creating a spatial condition in which ecological recovery and habitation can develop together.

The gradient maps were important in translating these relationships into spatial decisions. Rather than dividing the territory into fixed zones of safe and unsafe ground, the analysis produces transitional conditions in which degrees of saturation, stability, erosion, and ecological sensitivity inform different forms of intervention. This is particularly relevant in landslide-prone landscapes, where environmental conditions vary seasonally and can change substantially during extreme rainfall.

Discussion

These results should nevertheless be interpreted as design-oriented simulations rather than predictive models of future landslides. The Height-Field studies approximate flow behaviour and identify relative areas of accumulation, acceleration, and erosion, but they do not reproduce the full geological complexity of slope failure. Soil depth, subsurface groundwater conditions, and rainfall thresholds would require further calibration using site-specific hydrological and geotechnical data. The framework therefore establishes spatial tendencies that can guide design decisions, rather than claiming to determine precisely when or where failure will occur.

194

The development of HB-04 is significant because it achieved the strongest balance between water resistance and mechanical performance within the tested series. Its performance supports the continued investigation of RHA-based composites for landscape infrastructure, particularly where material composition can respond to local hydrological and structural demands. The fibre studies similarly suggest that reinforcement can be adjusted according to areas of greater flexural demand, establishing a direct relationship between material formulation and the tessellated channel geometry. The experiments remain limited by their scale, duration, and controlled testing conditions. Small specimens and short exposure periods cannot adequately represent the behaviour of fullscale components subjected to prolonged saturation, repeated wet-dry cycles, variable loading, erosion, weathering, and direct soil contact. The hydrophilic mixtures present a further limitation, since high water absorption was accompanied in some samples by material degradation, indicating that moisture retention cannot yet be separated reliably from loss of mechanical integrity. The recorded compressive strengths also remain insufficient to justify the material as a conventional structural substitute.

Future development should therefore focus on how the agroforestry framework can generate stronger morphological rules for growth. Rather than treating the final master plan as a fixed outcome, the urban structure could be tested through longerterm scenarios of consolidation and adaptation, allowing the research to evaluate how a community might progressively inhabit a landslide-prone territory without losing the ecological processes that make occupation possible.

Discussion

The hydrological simulations reinforce the central argument that the control and redistribution of water is fundamental to reducing instability. The branching network, terraces, retention areas, and bioremediation zones do not attempt to stop water, but to influence its velocity, concentration, and movement through the landscape. In this sense, resilience is understood less as resistance to environmental change and more as the capacity to redirect processes before they accumulate into critical conditions.

This becomes particularly evident in the transition from territorial analysis to the master plan. Programmes are not positioned independently of environmental conditions; their distribution changes according to stability, ecological function, infrastructure, and proximity to areas intended to accommodate water and erosion. The result challenges conventional master-planning approaches based on fixed parcels and boundaries, proposing instead a settlement structure capable of changing density, permanence, and morphology across the site.

The current research, however, resolves this relationship more clearly at the territorial scale than at the urban scale. The transition from environmental gradients to a coherent settlement morphology still requires further elaboration, particularly in defining how clusters consolidate, how open productive areas structure everyday urban life, and how the settlement changes as land conditions improve over time. This is necessary to move the proposal beyond environmental zoning toward a more developed model of urban form.

Further research is therefore required across multiple scales of testing. Material-level studies should establish repeatability, longer-term durability, and the stability of the binder and fibre systems under extended environmental exposure. Component-scale testing should examine joints, thickness variation, flexural behaviour, and failure mechanisms within the tessellated assemblies

195

Discussion Conclusion This research reframes landslide-prone territory as a condition to be negotiated rather than simply defended against. Instead of approaching instability as an exceptional event, the project considers it part of a longer and changing landscape process. This shifts the role of architecture from producing fixed solutions toward establishing forms of occupation that can respond to uncertainty and evolve with the territory. The project demonstrates that this shift requires design decisions to emerge from environmental behaviour rather than being imposed independently from it. Through simulation, spatial development, and material experimentation, the research establishes a methodology in which architecture is continuously tested against the conditions that support or constrain occupation. Its contribution therefore lies not in proposing a universal solution to landslide risk, but in defining a way of designing where instability itself becomes an active parameter in the development of settlement.

Discussion

At the same time, the research remains deliberately open-ended. The material system requires longer-term validation, while the urban and morphological implications need to be developed beyond the current master plan. Further work should examine how the settlement transforms through time and whether the proposed relationships can remain effective as environmental and social conditions change. This is essential if the framework is to progress from a design proposition toward a more transferable model for landslide-prone territories. Ultimately, the project questions the assumption that resilience must be achieved by making the landscape permanently secure. A more appropriate response may be to accept that some conditions will remain uncertain and to design accordingly. The ambition is therefore not to create a territory in which landslides can no longer occur, but one in which their possibility no longer dictates complete withdrawal or uncompromising control. Architecture becomes a means of establishing a more measured form of coexistence with instability, allowing settlement to endure by learning how, where, and when to change.

196

197

Discussion

199 198

Discussion

201 200

Appendix

APPENDIX

the image will change 202

203

Historical Precipitation Record October Rainfall Baseline for the Escazú Region, Costa Rica

San José, Costa Rica — October Monthly Precipitation Station: Juan Santamaría International Airport (WMO 78762 / GHCN ID CSM00078762) — 9.994°N, -84.209°W,

Year 1984 1986 1990 1992 1994 1996 2004 2005 2007 2008 2010 2012 2013 2014 2016 2017 2019 2021 2022 2023 2024

Total Precipitation (mm) 230.2 251 349.5 236 212.5 255.6 278.6 397.7 376.1 217 466 262.1 436.9 315.4 283.9 371.7 251 205.5 296 297.9 339.6

Total Precipitation (in) 9.062992126 9.881889764 13.75984252 9.291338583 8.366141732 10.06299213 10.96850394 15.65748031 14.80708661 8.543307087 18.34645669 10.31889764 17.2007874 12.41732283 11.17716535 14.63385827 9.881889764 8.090551181 11.65354331 11.72834646 13.37007874

Average Minimum Maximum Std Dev Years with d Years reques

301.4380952 205.5 466 74.95933215 21 51

11.86764154 8.090551181 18.34645669 2.951154809

Note: This station's automated/manual reporting has gaps — only years with a complete, quality-controlled monthly total from

Figure 8.1 Historical October Precipitation Recorded near Escazú, 1984–2024

204

Appendix

Source: NOAA NCEI Global Historical Climatology Network — Global Summary of the Month (GSOM), accessed

The dataset summarises recorded October precipitation for 21 years between 1984 and 2024. Monthly totals range from 205.5 mm to 466.0 mm, with an average of 301.4 mm, demonstrating considerable variation in seasonal rainfall intensity. Although the record is incomplete, it provides a regional climatic baseline for Escazú. The data informs the rainfall input, runoff assumptions and hydrological parameters used in the subsequent channel-generation and landscape simulation processes.

Station: Juan Santamaría International Airport (WMO 78762 / GHCN ID CSM00078762) — 9.994°N, -84.209°W, ~10 km NW of San José centro Source: NOAA NCEI, Global Historical Climatology Network – Global Summary of the Month (GSOM). Juan Santamaría International Airport Station, WMO 78762 / GHCN CSM00078762. Note: This station’s automated/manual reporting has gaps — only years with a complete, quality-controlled monthly total from NOAA are listed. 21 of 51 requested years (1975-2025) had usable October data; missing years are NOT included as zero, they are simply absent from the record.

205

Parametric Environmental Influence-Field Individual Factor Maps and Assigned Weights Active landslides

Aquifer

X 1.5

10.00

9.00

8.00

7.00

6.00

5.00

4.00

3.00

2.00

1.00

0.00

Avalanch zones

Fault lines

X 8.0

X 2.0

10.00

9.00

8.00

7.00

6.00

5.00

4.00

3.00

2.00

1.00

0.00

X 2.0

X 10.0

Appendix

Soil type

10.00

9.00

8.00

7.00

6.00

5.00

4.00

3.00

2.00

1.00

0.00

Higher-weighted factors, including slope, soil type, protected areas, rivers and landslide conditions, exert a stronger influence on the combined evaluation. Together, these layers establish the environmental criteria used to calculate the composite intervention-priority map.

Selection parameter High

Protected area boundary

X 2.0

X 10.0

10.00

9.00

8.00

7.00

6.00

5.00

4.00

3.00

2.00

1.00

0.00

Rivers

Low

Slope

X 8.0

206

The site analysis translates ten environmental datasets into a series of comparable spatial gradients. Each layer is normalised to a common numerical range and assigned a weighting coefficient according to its influence on hydrological risk, ecological protection and development suitability.

Appendix

Geological

Precipitation

Composite Intervention-Priority Map

X 10.0

10.00

9.00

8.00

7.00

6.00

5.00

4.00

3.00

2.00

1.00

0.00

Figure 8.3 Composite Weighted Intervention-Priority Map of Escazú, reproduced from Figure 3.4

The computational model combines the previously weighted environmental layers into a continuous intervention-priority field. Each normalised value is multiplied by its assigned coefficient and aggregated across the site, translating geographic conditions into numerical parameters for subsequent design.

Figure 8.2 Normalised Environmental Factor Maps and Weighting Coefficients for Escazú

207

Definition - Branching Channel Propagation

Custom C# Scripts - Branching Channel Propagation #region Usings

Input

Output

SamplePoints

Locations at which the spatial field is evaluated

Attractors

Locations generating spatial influence

Weights

Relative influence assigned to each attractor

Radius

Reference radius used to normalise distance

Decay

Exponent controlling how rapidly influence decreases with distance

FieldValues

An effective-distance value representing the combined influence at each sample point

using System; using System.Collections.Generic; using Rhino.Geometry; using Grasshopper.Kernel;

double fieldValue = totalPotential > 0.0 ? r / Math.Pow(totalPotential, 1.0 / p) : r * 2.0;

#endregion public class Script_Instance : GH_ScriptInstance { private void RunScript( List<Point3d> SamplePoints, List<Point3d> Attractors, List<double> Weights, double Radius, double Decay, ref object FieldValues) { // Validate sample points if (SamplePoints == null || SamplePoints.Count == 0) { FieldValues = new List<double>(); return; }

} }

results.Add(fieldValue);

FieldValues = results;

// Return the weight assigned to an attractor private static double GetWeight( List<double> weights, int index) { if (weights == null || weights.Count == 0) return 1.0; if (index < weights.Count) return weights[index];

}

return weights[weights.Count - 1];

Appendix

// Return zero values when no attractors are provided if (Attractors == null || Attractors.Count == 0) { FieldValues = new List<double>( new double[SamplePoints.Count] ); return; }

Appendix

}

weight / Math.Pow(normalisedDistance, p);

// Set safe parameter values double p = Math.Abs(Decay) < 0.001 ? 1.0 : Decay; double r = Math.Abs(Radius) < 0.001 ? 1.0 : Radius; List<double> results = new List<double>(SamplePoints.Count); // Calculate the field value for each sample point foreach (Point3d samplePoint in SamplePoints) { double totalPotential = 0.0; for (int i = 0; i < Attractors.Count; i++) { double distance = samplePoint.DistanceTo(Attractors[i]); double weight = GetWeight(Weights, i); double normalisedDistance = Math.Max(distance / r, 0.01); totalPotential +=

The script translates the previously weighted environmental data into a continuous spatial influence field. At each sample point, it calculates the combined effect of all attractors using their assigned weights, influence radius and decay rate. The resulting effective-distance values are mapped onto the site mesh to support spatial evaluation and subsequent parametric allocation.

208

209

Channel Network Development Terrain-Responsive Channel Network Outcomes

Notes.

Appendix

Initial seeds = 17 Total agent = 741 Growing = 29 Blocked = 90 DIssipated = 520 Merged = 102 Maximum width = 24m Minimum width = 2.1m

LEGEND water channel starting seeds contour line water channel existing river natural flooding path

Beginning from a series of water-channel seeds, the computational model propagates channel agents across the terrain according to slope, flow direction, discharge and spatial constraints. Each branch continuously updates its hydraulic state and is classified as growing, merged, blocked or dissipated. The resulting network adapts its route and width to local conditions, connects viable branches with existing rivers, and redirects unresolved flows towards distributed storage and terrace-retention locations. 210

Figure 8.4 Resolved Channel Paths and Storage Outcomes across the Escazú Terrain, reproduced from Figure 3.13

211

Channel - Section Typology Study

C ha nne l t y p e V - s ha p e d (t ri a ng ul a r)

S e c t i o na l i m a g e

H y d ra ul i c e ffi c i e nc y Lowest hydraulic radius of the common shapes; efficient only

Flow c a pa c ity Small

for small discharges, but low flows stay concentrated at the

V e l o c i t y b e ha v i o ur

T e rra i n / t o p o g ra p hy Roadsides, narrow berms, small

S o i l & g ro und c o nd i t i o ns

Simplest section to excavate and form on a slope; no vertical walls, so almost no lateral earth-

given slope - flow concentrates at the apex

pressure demand on the lining. The apex is a stress concentration - thicken or round the invert. Easily upland catchments

rise quickly, so restrict to small

keyed into a slope face or berm.

flows

invert, which keeps the channel self-cleansing.

T ra p e z o i d a l

S t ruc t ura l fe a t ure s w he n b ui l t o n s l o p e s

Highest velocities of the basic shapes for a

S e d i m e nt & s e l f- c l e a ns i ng

E ro s i o n / s c o ur ri s k o n t he c ha nne l

Cohesive or lined soils; velocities Excellent at low flow (flow stays deep High at the apex; velocity checks needed at invert)

above ~2 m/s on unlined ground

The practical 'gold standard': closest buildable approximation L a r g e - t h e h i g h - c a p a c i t y w o r k h o r s e

Moderate, uniform; velocity controlled via

Inc l i ne d s i d e s a re s e l f- s up p o rt i ng , s o a t hi n unre i nfo rc e d l i ni ng i s fe a s i b l e . B a s e m us t b e

Moderate slopes, valley floors, crest

Nearly all soils; side slopes

Good; flat bottom can silt at very low Moderate; protect side-slope toes; weep

of the semicircle in soil. High capacity per excavated volume.

bottom width and slope

b e nc he d l e v e l i nt o t he s l o p e ; p ro v i d e w e e p ho l e s t o re l i e v e up l i ft o n s a t ura t e d g ro und .

plateaus with room for the top width

flattened in weak ground

flow

holes prevent lining uplift

Corners trap sediment at low flow

Wall-base joint is the classic crack/leak

L i ni ng c a n b e a s s e m b l e d fro m fl a t - c a s t p a ne l s .

R e c t a ng ul a r

Good efficiency (half-square); compact top width for a given

Large per unit top width

capacity.

Moderate; sharp corners create local

Vertical walls behave as small retaining walls and must resist lateral earth and water pressure - they

Narrow benches and space-

Needs firm, uniform foundation;

turbulence

need reinforcement or generous thickness. Valued on narrow benches where width is scarce.

constrained cut slopes

poor on soft or moving ground

Requires firm, uniform founding; sensitive to differential settlement on moving ground.

U - s ha p e d (ro und e d i nv e rt )

S e m i c i rc ul a r (ha l f- ro und )

Near-semicircular efficiency; the rounded invert maintains

R o und e d i nv e rt k e e p s l o w fl o w s fa s t -

The standard slope-berm drain in benched-slope practice (e.g. Hong Kong GEO). Precast segments

Benched/terraced slopes; standard

All soils with a mortar/granular

V e ry g o o d ; m i ni m a l d e a d

L o w ; s m o o t h g e o m e t ry , fe w s t re s s

velocity and self-cleansing at low flow better than a flat-

g o o d v e l o c i t y m a i nt e na nc e a c ro s s fl o w

bed onto a mortar screed; rounded invert avoids the cracking-prone corners of rectangular sections.

detail on engineered cuts

bedding layer

c o rne rs

bottomed section.

ra ng e

Compact and shallow - small excavation into the bench.

B e s t v e l o c i t y d i s t ri b ut i o n o f a ny s ha p e ;

A rc h g e o m e t ry c a rri e s g ro und a nd ha nd l i ng l o a d s i n c o m p re s s i o n - i d e a l fo r l o w - t e ns i l e -

M o d e ra t e t o s t e e p t e rra i n, s m a l l -

Light units, low bearing pressure E x c e l l e n t s e l f - c l e a n s i n g

L o w e s t s c o ur ri s k - no c o rne rs ,

no d e a d z o ne s

s t re ng t h l i ni ng s . S ha l l o w , l i g ht uni t s w i t h l o w b e a ri ng d e m a nd s ui t w e a k o r d i s t urb e d s l o p e m e d i um fl o w s

- tolerate weak or disturbed

uni fo rm s he a r a ro und t he p e ri m e t e r

s o i l s . C a p a c i t y l i m i t e d b y t he s ha l l o w d e p t h; c a s c a d e s e v e ra l i n p a ra l l e l i f ne e d e d .

soils

Low velocity design (grass limit ~1-1.5 m/s);

Normally unlined and vegetated, so it infiltrates water into the ground - keep OFF the landslide body; Gentle, STABLE terrain only

Permeable soils infiltrate - never Grass traps sediment - periodic

Grass protects up to its design velocity,

grass adds roughness

use only on stable ground upslope as a diversion. No sharp breaks in the section, so the sides do not

on the landslide mass itself

removal needed

then fails progressively

T he t he o re t i c a l o p t i m um : m a x i m um hy d ra ul i c ra d i us

Medium

location

unless reinforced

Small-medium (depth-limited)

(fl o w a re a p e r w e t t e d p e ri m e t e r) o f a ny o p e n s e c t i o n, he nc e hi g he s t d i s c ha rg e fo r a g i v e n a re a , s l o p e a nd

The study compares a range of channel-section precedents according to their geometry, hydraulic performance, construction requirements and suitability for the Escazú terrain. Through this comparative evaluation, the semicircular section was selected as the preferred profile due to its ability to accommodate variable flow conditions while maintaining a continuous and hydraulically efficient channel form. The selected section provides the basis for the subsequent dimensional and velocity analysis.

P a ra b o l i c (g ra s s e d s w a l e )

High efficiency, close to semicircular; velocity distributed

Medium

smoothly so grass lining survives.

Appendix

ro ug hne s s .

slump; blends with terrain and adds root cohesion.

D i s h / s a uc e r c ha nne l

Low capacity because depth is small; velocities stay low and

Small

Very low, shallow sheet-like flow

uniform.

Minimal excavation - almost surface-laid, so it disturbs a marginal slope least. Traversable by people

Benched terraces, paths, plaza-like flat Any; nearly surface-laid so

Silts easily; relies on frequent small

Very low - low velocity and uniform

and light equipment. Thin lining of uniform curvature with no stress corners; ideal as bench/berm

areas

minimal disturbance

flows or sweeping

curvature

Slope toe, valley collectors

Standard bedding; needs width

Best sediment behaviour across

Step corners need protection; otherwise

variable flows

moderate

S t e p s t ra p d e b ri s - d e s i g n fo r

Highest demand: impact + abrasion on

surface drainage on stepped slopes.

Co mp o s it e t wo - s t a g e

Efficient across a wide flow range: the inner channel keeps

Small to very large (staged)

dry-weather flow deep enough to self-cleanse; the outer

Inner stage keeps dry-season flow fast; storm More complex formwork; the internal steps are stress corners that need thickening. Worth it where flow spreads and slows on the upper stage

stage takes storm flow.

S t e p p e d c a s c a d e / c hut e

Steps dissipate energy continuously, so the terminal velocity

tropical rainfall makes flows highly variable. Bench the full width into the slope; the wide shallow upper stage keeps excavation depth modest.

Energy dissipated step by step; prevents the

Must be anchored against sliding: cut-off keys or anchor blocks every few metres, plus a stilling basin S t e e p f a c e s ; t h e c o n n e c t o r D O W N Any soil, but anchorage/keys

at the toe is far lower than a smooth chute; prevents

Medium-large

destructive supercritical velocities of smooth

/ energy dissipator at the toe. Joints must tolerate slope movement. Highest abrasion and impact

supercritical flow damage.

chutes

demand of all the types - use densest mix and thickest section.

Low per branch by design

Shallow and surface-parallel, needing almost no cutting - suits marginally stable faces. Usually

t he s l o p e

essential; scour protection at toe o v e r t o p p i n g w i t h o u t d a m a g e

every step edge; armour edges

Open slope faces, landslide bodies

C he v ro n / he rri ng b o ne s l o p e -

Each branch is small, but the pattern removes surface water

Residual and weathered volcanic Branches self-cleanse into the spine

Low per branch; spine takes the

fa c e d ra i ns

quickly over the whole face and shortens overland-flow paths

combined with vegetation between branches. Branches discharge to a stepped spine (see cascade).

soils; works on marginally stable

aggregate load

- the goal on a landslide body is minimum residence time.

Standard remedial detail for slid masses.

faces

C o v e re d t re nc h / s l o t d ra i n

Hydraulics of a rectangular channel; cover excludes coarse

Small per branch; large in aggregate

Medium-large

debris and allows traffic over the line.

As rectangular; cover has no hydraulic role

Cover slab or grating carries traffic loads in bending - a significant structural demand. Use where the

until clogged

channel crosses paths, roads or work benches. Blockages are hidden, so inspection openings are

Trafficked areas, road/path crossings

Firm foundation for cover loads

Grating intercepts coarse debris -

Low internal; grate blockage is the real

deliberate trap

risk

essential.

Figure 8.5 Channel-section typology matrix for variable terrain and flow conditions

212

213

Diversion Angle–Velocity Training Dataset

Appendix

2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m

8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 5.0m 5.102m 5.204m 5.306m 5.408m 5.51m 5.612m 5.714m 5.816m 5.918m 6.02m 6.122m 6.224m 6.327m 6.429m 6.531m 6.633m 6.735m 6.837m 6.939m 7.041m 7.143m 7.245m 7.347m 7.449m 7.551m 7.653m 7.755m 7.857m 7.959m 8.061m 8.163m 8.265m 8.367m 8.469m 8.571m 8.673m 8.776m 8.878m 8.98m 9.082m

2.0m 8.0m 30 0.5 Synthetic - Angle 7 m/s 7.7885 2.0m 8.0m 31.22 0.5 Synthetic - Angle 7 m/s 8.8148 2.0m 8.0m 32.45 0.5 Synthetic - Angle 7 m/s 8.2392 2.0m 8.0m 33.67 0.5 Synthetic - Angle 7 m/s 8.942 2.0m 8.0m 34.898 0.5 Synthetic - Angle 7 m/s 8.6137 2.0m 8.0m 36.122 0.5 Synthetic - Angle 7 m/s 7.27 2.0m 8.0m 37.347 0.5 Synthetic - Angle 7 m/s 8.5701 2.0m 8.0m 38.571 0.5 Synthetic - Angle 7 m/s 8.4672 2.0m 8.0m 39.796 0.5 Synthetic - Angle 7 m/s 8.2271 2.0m 8.0m 41.02 0.5 Synthetic - Angle 7 m/s 7.914 2.0m 8.0m 42.245 0.5 Synthetic - Angle 7 m/s 8.5406 2.0m 8.0m 43.469 0.5 Synthetic - Angle 7 m/s 9.0106 2.0m 8.0m 44.694 0.5 Synthetic - Angle 7 m/s 8.1292 2.0m 8.0m 45.918 0.5 Synthetic - Angle 7 m/s 7.5166 2.0m 8.0m 47.143 0.5 Synthetic - Angle 7 m/s 8.1382 2.0m 8.0m 48.367 0.5 Synthetic - Angle 7 m/s 8.0629 2.0m 8.0m 49.592 0.5 Synthetic - Angle 7 m/s 9.083 2.0m 8.0m 50.816 0.5 Synthetic - Angle 7 m/s 7.6958 2.0m 8.0m 52.041 0.5 Synthetic - Angle 7 m/s 7.7712 2.0m 8.0m 53.265 0.5 Synthetic - Angle 7 m/s 7.1293 2.0m 8.0m 54.49 0.5 Synthetic - Angle 7 m/s 8.4968 2.0m 8.0m 55.714 0.5 Synthetic - Angle 7 m/s 7.9493 2.0m 8.0m 56.939 0.5 Synthetic - Angle 7 m/s 6.6888 2.0m 8.0m 58.163 0.5 Synthetic - Angle 7 m/s 7.4054 2.0m 8.0m 59.388 0.5 Synthetic - Angle 7 m/s 8.4249 2.0m 8.0m 60.612 0.5 Synthetic - Angle 7 m/s 7.9742 2.0m 8.0m 61.837 0.5 Synthetic - Angle 7 m/s 6.8852 2.0m 8.0m 63.061 0.5 Synthetic - Angle 7 m/s 6.4644 2.0m 8.0m 64.286 0.5 Synthetic - Angle 7 m/s 7.5999 2.0m 8.0m 65.51 0.5 Synthetic - Angle 7 m/s 7.1398 2.0m 8.0m 66.735 0.5 Synthetic - Angle 7 m/s 7.7478 2.0m 8.0m 67.959 0.5 Synthetic - Angle 7 m/s 7.702 2.0m 8.0m 69.184 0.5 Synthetic - Angle 7 m/s 7.6641 2.0m 8.0m 70.408 0.5 Synthetic - Angle 7 m/s 7.0407 2.0m 8.0m 71.633 0.5 Synthetic - Angle 7 m/s 7.5723 2.0m 8.0m 72.857 0.5 Synthetic - Angle 7 m/s 8.0m 7.063 S-ANGLE- 2.0m 8.0m 2.0m 30 2.0m 8.0m 0.5 Synthetic - Angle 7 m/s 8.0m 7.2983 S-ANGLE-74.082 2.0m 8.0m 2.0m 31.22 2.0m 8.0m 0.5 Synthetic - Angle 7 m/s 8.0m 6.5636 S-ANGLE-75.306 2.0m 8.0m 2.0m 32.45 2.0m 8.0m 0.5 Synthetic - Angle 7 m/s 8.0m 6.5086 S-ANGLE-76.531 2.0m 8.0m 2.0m 33.67 S-ANGLE-77.755 2.0m 8.0m 2.0m 34.898 2.0m 8.0m 0.5 Synthetic - Angle 7 m/s 8.0m 6.5928 S-ANGLE- 78.98 2.0m 8.0m 2.0m 36.122 2.0m 8.0m 0.5 Synthetic - Angle 7 m/s 8.0m 7.1799 S-ANGLE-80.204 2.0m 8.0m 2.0m 37.347 2.0m 8.0m 0.5 Synthetic - Angle 7 m/s 8.0m 6.3215 S-ANGLE-81.429 2.0m 8.0m 2.0m 38.571 2.0m 8.0m 0.5 Synthetic - Angle 7 m/s 8.0m 6.0273 S-ANGLE- 2.0m 8.0m 2.0m 8.0m 39.796 2.0m 8.0m 82.653 0.5 Synthetic Angle 7 m/s 6.4616 S-ANGLE- 2.0m 8.0m 2.0m 8.0m 41.02 2.0m 8.0m 83.878 0.5 Synthetic Angle 7 m/s 5.2605 S-ANGLE- 2.0m 8.0m 2.0m 8.0m 42.245 2.0m 8.0m 85.102 0.5 Synthetic Angle 7 m/s 5.7446 S-ANGLE- 2.0m 8.0m 2.0m 8.0m 43.469 2.0m 8.0m 0.5 Synthetic - Angle 7 m/s 8.0m 5.4271 S-ANGLE-86.327 2.0m 8.0m 2.0m 44.694 S-ANGLE-87.551 2.0m 8.0m 2.0m 45.918 2.0m 8.0m 0.5 Synthetic - Angle 7 m/s 8.0m 5.4419 S-ANGLE2.0m 8.0m 2.0m 8.0m 47.143 2.0m 8.0m 88.776 0.5 Synthetic - Angle 7 m/s 5.2535 S-ANGLE2.0m 8.0m 2.0m 8.0m 48.367 2.0m 8.0m 90 0.5 Synthetic - Angle 7 m/s 6.1744 S-ANGLE- 2.0m 8.0m 2.0m 8.0m 49.592 2.0m 5.0m 45 0.5 Synthetic Width 7 m/s 8.4859 S-ANGLE- 2.0m 8.0m 2.0m 8.0m 50.816 2.0m 5.102m 45 0.5 Synthetic - Width 7 m/s 8.0m 9.2409 S-ANGLE- 2.0m 8.0m 2.0m 52.041 2.0m 5.204m 45 0.5 Synthetic - Width 7 m/s 8.0m 8.4737 S-ANGLE- 2.0m 8.0m 2.0m 53.265 2.0m 5.306m 45 0.5 Synthetic - Width 7 m/s 8.0m 10.5293 S-ANGLE- 2.0m 8.0m 2.0m 54.49 S-ANGLE- 2.0m 8.0m 2.0m 55.714 2.0m 5.408m 45 0.5 Synthetic - Width 7 m/s 8.0m 8.5922 S-ANGLE- 2.0m 8.0m 2.0m 56.939 2.0m 5.51m 45 0.5 Synthetic - Width 7 m/s 8.0m 7.9345 S-ANGLE- 2.0m 8.0m 2.0m 58.163 2.0m 5.612m 45 0.5 Synthetic - Width 7 m/s 8.0m 8.5214 S-ANGLE- 2.0m 8.0m 2.0m 59.388 2.0m 5.714m 45 0.5 Synthetic - Width 7 m/s 8.0m 7.9597 S-ANGLE- 2.0m 8.0m 2.0m 8.0m 60.612 2.0m 5.816m 45 0.5 Synthetic Width 7 m/s 9.2316 S-ANGLE- 2.0m 8.0m 2.0m 8.0m 61.837 Diversion angle-velocity 2.0m 5.918m 45 0.5 Synthetic Width 7 m/s 7.7611 S-ANGLE- 2.0m 8.0m 2.0m 8.0m 63.061 6 2.0m 6.02m 45 0.5 Synthetic Width 7 m/s 8.2136 S-ANGLE- 2.0m 8.0m 2.0m 8.0m 64.286 2.0m 6.122m 45 0.5 Synthetic - Width 7 m/s 8.0m 9.0045 S-ANGLE- 2.0m 8.0m 2.0m 65.51 S-ANGLE- 2.0m 8.0m 2.0m 66.735 2.0m 6.224m 45 0.5 Synthetic - Width 7 m/s 8.0m 9.1172 5 S-ANGLE- 2.0m 8.0m 2.0m 67.959 2.0m 6.327m 45 0.5 Synthetic - Width 7 m/s 8.0m 7.8323 S-ANGLE- 2.0m 8.0m 2.0m 69.184 2.0m 6.429m 45 0.5 Synthetic - Width 7 m/s 8.0m 6.8268 4 S-ANGLE- 2.0m 8.0m 2.0m 70.408 2.0m 6.531m 45 0.5 Synthetic - Width 7 m/s 8.0m 7.5837 S-ANGLE- 2.0m 8.0m 2.0m 8.0m 71.633 2.0m 6.633m 45 0.5 Synthetic Width 7 m/s 8.2606 3 S-ANGLE- 2.0m 8.0m 2.0m 8.0m 72.857 2.0m 6.735m 45 0.5 Synthetic - Width 7 m/s 8.0m 8.038 S-ANGLE- 2.0m 8.0m 2.0m 74.082 2.0m 6.837m 45 0.5 Synthetic - Width 7 m/s 8.0m 7.82 S-ANGLE2.0m 8.0m 2.0m 75.306 2 2.0m 6.939m 45 0.5 Synthetic - Width 7 m/s 8.0m 7.372 S-ANGLE- 2.0m 8.0m 2.0m 76.531 S-ANGLE- 2.0m 8.0m 2.0m 77.755 2.0m 7.041m 45 0.5 Synthetic - Width 7 m/s 8.0m 6.9768 1 S-ANGLE- 2.0m 8.0m 2.0m 78.98 2.0m 7.143m 45 0.5 Synthetic - Width 7 m/s 8.0m 7.7267 S-ANGLE- 2.0m 8.0m 2.0m 80.204 2.0m 7.245m 45 0.5 Synthetic - Width 7 m/s 8.0m 6.6958 S-ANGLE- 2.0m 8.0m 2.0m 8.0m 81.429 0 2.0m 7.347m 45 0.5 Synthetic - Width 7 m/s 8.0m 7.8362 S-ANGLE2.0m40 8.0m 82.653100 10 30 50 602.0m 70 80 90 2.0m 0 7.449m 20 S-ANGLE45 0.5 Synthetic Width 7 m/s 7.7355 2.0m 8.0m 2.0m 8.0m 83.878 2.0m 7.551m 45 0.5 Synthetic - Width 7 m/s 8.0m 7.7557 S-ANGLE- 2.0m 8.0m 2.0m 85.102 2.0m 7.653m 45 0.5 Synthetic - Width 7 m/s 8.0m 7.6153 S-ANGLE- 2.0m 8.0m 2.0m 86.327 2.0m 8.0m for 2.0m 87.551 2.0m Figure 8.6 7.755m 45 0.5 Synthetic - Width 7 m/s 8.0m DiversionS-ANGLEangle-velocity dataset machine-learning training 6.8908 S-ANGLE- 2.0m 8.0m 2.0m 88.776 2.0m 7.857m 45 0.5 Synthetic - Width 7 m/s 8.0m 7.7499 Widthvelocity S-ANGLE- 2.0m 8.0m 2.0m 90 2.0m 7.959m 45 0.5 Synthetic - Width 7 m/s 8.0m 7.2981 S-WIDTH- 2.0m 5.0m 2.0m 45 2.0m8 8.061m 45 0.5 Synthetic - Width 7 m/s 5.0m 8.5972 S-WIDTH- 2.0m 5.102m 2.0m 5.102m 45 2.0m 8.163m 45 0.5 Synthetic Width 7 m/s 7.923 S-WIDTH- 2.0m 5.204m 2.0m 5.204m 45 2.0m7 8.265m 45 0.5 Synthetic Width 7 m/s 6.5522 S-WIDTH- 2.0m 5.306m 2.0m 5.306m 45 2.0m 8.367m 45 0.5 Synthetic Width 7 m/s 6.7236 S-WIDTH- 2.0m 5.408m 2.0m 5.408m 45 2.0m6 8.469m 45 0.5 Synthetic - Width 7 m/s 5.51m 8.022 S-WIDTH- 2.0m 5.51m 2.0m 45 S-WIDTH- 2.0m 5.612m - Width 2.0m 45 2.0m 8.571m 45 0.5 Synthetic 7 m/s 5.612m 7.4789 S-WIDTH- 2.0m 5.714m - Width 2.0m 45 2.0m5 8.673m 45 0.5 Synthetic 7 m/s 5.714m 7.8125 S-WIDTH2.0m 5.816m 2.0m 5.816m 45 2.0m 8.776m 45 0.5 Synthetic - Width 7 m/s 8.4788 S-WIDTH- 2.0m 5.918m 2.0m 5.918m 45 2.0m 8.878m 45 0.5 Synthetic Width 7 m/s 7.7145 S-WIDTH- 2.0m 6.02m 2.0m 6.02m 45 4 2.0m 8.98m 45 0.5 Synthetic 7 m/s 6.122m 8.3423 S-WIDTH- 2.0m 6.122m - Width 2.0m 45 2.0m 9.082m 45 0.5 Synthetic 7 m/s 6.224m 8.2001 S-WIDTH- 2.0m 6.224m - Width 2.0m 45

8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 5.0m 5.102m 5.204m 5.306m 5.408m 5.51m 5.612m 5.714m 5.816m 5.918m 6.02m 6.122m 6.224m 6.327m 6.429m 6.531m 6.633m 6.735m 20 6.837m 6.939m 7.041m 7.143m 7.245m 7.347m 7.449m 7.551m 7.653m 7.755m 7.857m 7.959m 8.061m 8.163m 8.265m 8.367m 8.469m 8.571m 8.673m 8.776m 8.878m 8.98m 9.082m 9.184m 9.286m 9.388m 9.49m 9.592m 10 9.694m 9.796m 9.898m 10.0m

2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 302.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 2.0m 20 2.0m 2.0m 2.0m 2.0m

8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 8.0m 5.0m Diversion angle-velocity 5.102m 5.204m 5.306m 5.408m 5.51m 5.612m 5.714m 5.816m 5.918m 6.02m 6.122m 6.224m 6.327m 6.429m 6.531m 6.633m 6.735m 40 50 60 6.837m 6.939m 7.041m 7.143m 7.245m 7.347m Widthvelocity 7.449m 7.551m 7.653m 7.755m 7.857m 7.959m 8.061m 8.163m 8.265m 8.367m 8.469m 8.571m 8.673m 8.776m 6 8.878m 8.98m 5 9.082m 9.184m 4 9.286m 9.388m 9.49m 3 9.592m 30 9.694m 2 9.796m 9.898m 1 10.0m

70.408 0.5 Synthetic - Angle 71.633 0.5 Synthetic - Angle 72.857 0.5 Synthetic - Angle 74.082 0.5 Synthetic - Angle 75.306 0.5 Synthetic - Angle 76.531 0.5 Synthetic - Angle 77.755 0.5 Synthetic - Angle 78.98 0.5 Synthetic - Angle 80.204 0.5 Synthetic - Angle 81.429 0.5 Synthetic - Angle 82.653 0.5 Synthetic - Angle 83.878 0.5 Synthetic - Angle 85.102 0.5 Synthetic - Angle 86.327 0.5 Synthetic - Angle 87.551 0.5 Synthetic - Angle 88.776 0.5 Synthetic - Angle 90 0.5 Synthetic - Angle 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 80 90 4570 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 Diversion 0.5 Synthetic - Width angle-velocity 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 40 50 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width 45 0.5 Synthetic - Width

Channel Width–Velocity Training Dataset

4.4203 4.6682 4.0726 4.4427 4.3491 4.6349 4.6246 4.6004 4.1115 4.3573 4.6881 4.3765 4.514 4.2712 4.6088 4.3758 4.1053 4.022 3.6691 3.9766 3.7687 3.5811 3.4039 3.2798 3.1222 3.105 3.0611 3.0078 3.0213 2.909 2.74 2.7645 2.88 2.7818 2.6884 2.8084 0.5 Synthetic - Angle 2.7399 - Angle 0.5 Synthetic 2.6619 - Angle 0.5 Synthetic 2.6949 - Angle 0.5 Synthetic 0.5 Synthetic 2.5547 - Angle 0.5 Synthetic 2.6601 - Angle 0.5 Synthetic 2.658 - Angle 0.5 Synthetic 2.5169 - Angle 0.5 Synthetic - Angle 2.6699 - Angle 0.5 Synthetic 2.5086 - Angle 0.5 Synthetic 2.4124 - Angle 0.5 Synthetic 2.4481 - Angle 0.5 Synthetic 0.5 Synthetic 2.5587 - Angle 0.5 Synthetic 2.4528 - Angle 0.5 Synthetic 2.5191 - Angle 0.5 Synthetic - Angle 4.3475 0.5 Synthetic - Angle 3.9212 - Angle 0.5 Synthetic 4.0513 - Angle 0.5 Synthetic 3.7396 - Angle 0.5 Synthetic 0.5 Synthetic 4.0442 - Angle 0.5 Synthetic 3.6973 - Angle 0.5 Synthetic 3.6885 - Angle 0.5 Synthetic 3.7014 - Angle 0.5 Synthetic - Angle 3.6582 0.5 Synthetic - Angle 3.8645 - Angle 0.5 Synthetic 3.5192 - Angle 0.5 Synthetic 3.7215 - Angle 0.5 Synthetic 0.5 Synthetic 3.8104 - Angle 0.5 Synthetic 3.8513 - Angle 0.5 Synthetic 3.9815 - Angle 0.5 Synthetic - Angle 3.9141 0.5 Synthetic - Angle 4.0299 - Angle 0.5 Synthetic 3.9271 - Angle 0.5 Synthetic 4.1146 - Angle 0.5 Synthetic 3.8963 - Angle 0.5 Synthetic 0.5 Synthetic 4.0252 - Angle 0.5 Synthetic 3.9232 - Angle 0.5 Synthetic 4.2623 - Angle 0.5 Synthetic - Angle 4.1116 0.5 Synthetic - Angle 4.405 - Angle 0.5 Synthetic 4.2323 - Angle 0.5 Synthetic 4.0982 - Angle 0.5 Synthetic 0.5 Synthetic 4.0173 - Angle 0.5 Synthetic 4.4724 - Angle 0.5 Synthetic 4.1172 - Angle 0.5 Synthetic 4.4749 - Width 0.5 Synthetic - Width 4.2727 - Width 0.5 Synthetic 4.1168 - Width 0.5 Synthetic 3.8995 - Width 0.5 Synthetic 4.2621 - Width 0.5 Synthetic 0.5 Synthetic 4.5582 - Width 0.5 Synthetic 4.6714 - Width 0.5 Synthetic 4.1003 - Width 0.5 Synthetic - Width 4.5234 0.5 Synthetic - Width 4.8118 - Width 0.5 Synthetic 4.2075 - Width 0.5 Synthetic

3.3682 4.1466 4.1666 4.4993 4.2646 2.6351 3.9455 3.8668 4.1156 3.5567 3.8525 4.6341 3.6152 3.2454 3.5294 3.6871 4.9777 3.6738 4.1021 3.1527 4.7281 4.3682 3.2849 4.1256 5.3027 4.8692 3.8241 3.4566 4.5786 4.2308 5.0078 4.9375 4.7841 4.2589 4.8839 4.2546 7 m/s 4.5584 7 m/s 3.9017 7 m/s 3.8137 7 m/s 7 m/s 4.0381 7 m/s 4.5198 7 m/s 3.6635 7 m/s 3.5104 7 m/s 3.7917 7 m/s 2.7519 7 m/s 3.3322 7 m/s 72.979 m/s 7 m/s 2.8832 7 m/s 2.8007 7 m/s 3.6553 7 m/s 4.1384 7 m/s 5.3197 7 m/s 4.4224 7 m/s 6.7897 7 m/s 74.548 m/s 7 m/s 4.2372 7 m/s 4.8329 7 m/s 4.2583 7 m/s 5.5734 7 m/s 3.8966 7 m/s 4.6944 7 m/s 75.283 m/s 7 m/s 5.3068 73.981 m/s 7 m/s 2.8453 7 m/s 3.6696 7 m/s 4.2307 7 m/s 4.1109 7 m/s 3.7054 7 m/s 3.4757 7 m/s 7 m/s 2.9516 7 m/s 3.8035 7 m/s 2.4335 7 m/s 3.7246 7 m/s 3.3305 7 m/s 3.5234 7 m/s 3.5171 7 m/s 7 m/s 2.8735 7 m/s 3.2775 7 m/s 3.1809 7 m/s 4.1223 7 m/s 3.6503 7 m/s 2.4354 7 m/s 2.8241 7 m/s 3.7599 7 m/s 7 m/s 2.9207 7 m/s 3.1411 7 m/s 4.3785 7 m/s 3.1911 7 m/s 3.5305 7 m/s 3.9926 7 m/s

2.7864 2.4852 2.4532 2.2769 2.3191 2.6829 2.0858 2.6217 2.4038 2.3359 2.1571 2.3025 2.8504 2.7955 2.0767 2.3375 2.9169 2.6141 1.8122 2.5777 2.9806 2.7982 2.8496 2.7577 3.0574 3.3852 2.9527 3.2555 3.0314 2.9595 2.9837 2.5281 3.1196 2.6064 3.0138 2.5192 7.7885 2.8931 8.8148 2.2306 8.2392 2.6392 8.942 2.6058 8.6137 1.95 7.27 2.0041 8.5701 2.4035 8.4672 8.2271 2.4455 7.914 1.818 8.5406 2.1005 9.0106 1.8768 8.1292 1.8186 7.5166 1.7763 8.1382 1.4567 8.0629 9.083 3.3597 7.6958 3.1052 7.7712 3.0621 7.1293 2.915 8.4968 3.3109 7.9493 2.8921 6.6888 2.9218 7.4054 3.126 8.4249 7.9742 2.9673 6.8852 2.6884 6.4644 2.7839 7.5999 2.7652 7.1398 3.0791 7.7478 2.9359 7.702 2.752 7.6641 3.1243 7.0407 7.5723 2.9787 7.063 2.9782 7.2983 2.9224 6.5636 3.1084 6.5086 3.0969 6.5928 2.93 7.1799 2.3972 6.3215 6.0273 3.1836 6.4616 2.9061 5.2605 2.6092 5.7446 3.0989 5.4271 2.859 5.4419 3.1323 5.2535 2.5194 6.1744 3.0572 8.4859 9.2409 2.5955 8.4737 3.195310.5293 3.413 8.5922 2.5482 7.9345 2.7693 8.5214 2.7505 7.9597 3.278 9.2316 2.6274 7.7611 8.2136 2.8306 9.0045 3.0378 9.1172

4.4203 4.6682 4.0726 4.4427 4.3491 4.6349 4.6246 4.6004 4.1115 4.3573 4.6881 4.3765 4.514 4.2712 4.6088 4.3758 4.1053 4.022 3.6691 3.9766 3.7687 3.5811 3.4039 3.2798 3.1222 3.105 3.0611 3.0078 3.0213 2.909 2.74 2.7645 2.88 2.7818 2.6884 2.8084 2.7399 2.6619 2.6949 2.5547 2.6601 2.658 2.5169 2.6699 2.5086 2.4124 2.4481 2.5587 2.4528 2.5191 4.3475 3.9212 4.0513 3.7396 4.0442 3.6973 3.6885 3.7014 3.6582 3.8645 3.5192 3.7215 3.8104

4.9777 3.6738 4.1021 3.1527 4.7281 4.3682 3.2849 4.1256 5.3027 4.8692 3.8241 3.4566 4.5786 4.2308 5.0078 4.9375 4.7841 4.2589 4.8839 4.2546 4.5584 3.9017 3.8137 4.0381 4.5198 3.6635 3.5104 3.7917 2.7519 3.3322 2.979 2.8832 2.8007 3.6553 4.1384 5.3197 4.4224 6.7897 4.548 4.2372 4.8329 4.2583 5.5734 3.8966 4.6944 5.283 5.3068

2.9169 2.6141 1.8122 2.5777 2.9806 2.7982 2.8496 2.7577 3.0574 3.3852 2.9527 3.2555 3.0314 2.9595 2.9837 2.5281 3.1196 2.6064 3.0138 2.5192 2.8931 2.2306 2.6392 2.6058 1.95 2.0041 2.4035 2.4455 1.818 2.1005 1.8768 1.8186 1.7763 1.4567 3.3597 3.1052 3.0621 2.915 3.3109 2.8921 2.9218 3.126 2.9673 2.6884 2.7839 2.7652 3.0791

7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 100 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 7 m/s 60 7 m/s 7 m/s 7 m/s 7 m/s

7.0407 7.5723 7.063 7.2983 6.5636 6.5086 6.5928 7.1799 6.3215 6.0273 6.4616 5.2605 5.7446 5.4271 5.4419 5.2535 6.1744 8.4859 9.2409 8.4737 10.5293 8.5922 7.9345 8.5214 7.9597 9.2316 7.7611 8.2136 9.0045 9.1172 7.8323 6.8268 7.5837 8.2606 8.038 7.82 7.372 6.9768 7.7267 6.6958 7.8362 7.7355 7.7557 7.6153 6.8908 7.7499 7.2981 8.5972 7.923 6.5522 6.7236 8.022 7.4789 7.8125 8.4788 7.7145 8.3423 8.2001 8.4658 7.5413 7.6803 8.0879 7.1307 7.9749 8.1383 8.3831 7.9633

2.7818 2.6884 2.8084 2.7399 2.6619 2.6949 2.5547 2.6601 2.658 2.5169 2.6699 2.5086 2.4124 2.4481 2.5587 2.4528 2.5191 4.3475 3.9212 4.0513 3.7396 4.0442 3.6973 3.6885 3.7014 3.6582 3.8645 3.5192 3.7215 3.8104 3.8513 3.9815 3.9141 4.0299 3.9271 4.1146 3.8963 4.0252 3.9232 4.2623 4.1116 4.405 4.2323 4.0982 4.0173 4.4724 4.1172 4.4749 4.2727 4.1168 3.8995 4.2621 4.5582 4.6714 4.1003 4.5234 4.8118 4.2075 4.5384 4.3478 4.4295 4.6396 4.5532 4.5153 4.5246 4.5562 4.7546

4.2589 4.8839 4.2546 4.5584 3.9017 3.8137 4.0381 4.5198 3.6635 3.5104 3.7917 2.7519 3.3322 2.979 2.8832 2.8007 3.6553 4.1384 5.3197 4.4224 6.7897 4.548 4.2372 4.8329 4.2583 5.5734 3.8966 4.6944 5.283 5.3068 3.981 2.8453 3.6696 4.2307 4.1109 3.7054 3.4757 2.9516 3.8035 2.4335 3.7246 3.3305 3.5234 3.5171 2.8735 3.2775 3.1809 4.1223 3.6503 2.4354 2.8241 3.7599 2.9207 3.1411 4.3785 3.1911 3.5305 3.9926 3.9274 3.1935 3.2508 3.4483 2.5775 3.4596 3.6137 3.8269 3.2087

2.6064 3.0138 2.5192 2.8931 2.2306 2.6392 2.6058 1.95 2.0041 2.4035 2.4455 1.818 2.1005 1.8768 1.8186 1.7763 1.4567 3.3597 3.1052 3.0621 2.915 3.3109 2.8921 2.9218 3.126 2.9673 2.6884 2.7839 2.7652 3.0791 2.9359 2.752 3.1243 2.9787 2.9782 2.9224 3.1084 3.0969 2.93 2.3972 3.1836 2.9061 2.6092 3.0989 2.859 3.1323 2.5194 3.0572 2.5955 3.1953 3.413 2.5482 2.7693 2.7505 3.278 2.6274 2.8306 3.0378 2.6424 2.9555 3.0742 2.8814 3.0042 3.3315 3.4125 3.1054 3.3673

Appendix

S-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-ANGLES-WIDTHS-WIDTHS-WIDTHS-WIDTHS-WIDTHS-WIDTHS-WIDTHS-WIDTH3.3682 S-WIDTH2.7864 4.1466 S-WIDTH2.4852 4.1666 S-WIDTH2.4532 4.4993 2.2769 S-WIDTH4.2646 2.3191 S-WIDTH2.6351 2.6829 3.9455 S-WIDTH2.0858 S-WIDTH3.8668 2.6217 4.1156 S-WIDTH2.4038 3.5567 S-WIDTH2.3359 3.8525 S-WIDTH2.1571 4.6341 S-WIDTH2.3025 3.6152 2.8504 S-WIDTH3.2454 2.7955 3.5294 S-WIDTH2.0767 3.6871 S-WIDTH2.3375 4.9777 S-WIDTH2.9169 3.6738 S-WIDTH2.6141 4.1021 S-WIDTH1.8122 3.1527 S-WIDTH2.5777 4.7281 2.9806 S-WIDTH4.3682 2.7982 S-WIDTH3.2849 2.8496 S-WIDTH4.1256 2.7577 S-WIDTH5.3027 3.0574 4.8692 S-WIDTH3.3852 3.8241 S-WIDTH2.9527 3.4566 S-WIDTH3.2555 4.5786 S-WIDTH3.0314 4.2308 2.9595 S-WIDTH5.0078 2.9837 214 S-WIDTH4.9375 2.5281 4.7841 S-WIDTH3.1196 4.2589 S-WIDTH2.6064 4.8839 S-WIDTH3.0138 4.2546 S-WIDTH2.5192 4.5584 S-WIDTH2.8931

S-ANGLE- 2.0m S-ANGLE- 2.0m S-ANGLE- 2.0m S-ANGLE- 2.0m S-ANGLE- 2.0m S-ANGLE- 2.0m S-ANGLE- 2.0m S-ANGLE- 2.0m S-ANGLE- 2.0m S-ANGLE- 2.0m S-ANGLE- 2.0m S-ANGLE- 2.0m S-ANGLE- 2.0m S-ANGLE- 2.0m S-ANGLE- 2.0m S-ANGLE- 2.0m S-ANGLE- 2.0m S-WIDTH- 2.0m S-WIDTH- 2.0m S-WIDTH2.0m 6 S-WIDTH- 2.0m S-WIDTH- 2.0m 5 S-WIDTH2.0m S-WIDTH- 2.0m S-WIDTH2.0m 4 S-WIDTH- 2.0m S-WIDTH- 2.0m 3 S-WIDTH2.0m S-WIDTH- 2.0m S-WIDTH2.0m 2 S-WIDTH- 2.0m S-WIDTH- 2.0m 1 S-WIDTH2.0m S-WIDTH- 2.0m S-WIDTH2.0m 0 0 S-WIDTH2.0m10 S-WIDTH- 2.0m S-WIDTH- 2.0m S-WIDTH- 2.0m S-WIDTH- 2.0m S-WIDTH- 2.0m S-WIDTH- 2.0m 8 S-WIDTH2.0m S-WIDTH- 2.0m S-WIDTH2.0m 7 S-WIDTH- 2.0m S-WIDTH- 2.0m 6 S-WIDTH2.0m S-WIDTH- 2.0m S-WIDTH2.0m 5 S-WIDTH- 2.0m S-WIDTH- 2.0m 4 S-WIDTH2.0m 3.3682S-WIDTH2.78642.0m 4.1466 2.4852 3 2.45322.0m 4.1666S-WIDTH4.4993S-WIDTH2.27692.0m 4.2646S-WIDTH2.31912.0m 2 2.68292.0m 2.6351S-WIDTH3.9455S-WIDTH2.08582.0m 3.8668S-WIDTH2.62172.0m 1 4.1156S-WIDTH2.40382.0m 3.5567 2.3359 S-WIDTH2.0m 3.8525 0 2.15712.0m S-WIDTH4.6341 02.3025 3.6152S-WIDTH2.85042.0m 3.2454S-WIDTH2.79552.0m 3.5294S-WIDTH2.07672.0m 3.6871S-WIDTH2.33752.0m

0 0

100

Width- velocity 8 7 6 5 4 3 2 1

0 0

Figure 8.7 Channel width-velocity dataset for machine-learning training

215

ng Velocity Ending velocity 7 2.4814 7 2.4002 7 2.4511 7 2.3393 7 2.4071 7 2.445 7 2.5021 7 2.446 7 2.3856 7 2.5015 7 2.4249 7 2.4864 7 2.4353 7 2.3949 7 2.5002 7 2.4464 7 2.5334 7 2.4329 7 2.4251 7 2.4802 7 2.4642 7 2.5032 7 2.5137 7 2.5226 7 2.4623 7 2.5091 7 2.4926 7 2.4247 7 2.5508 7 2.5068 7 2.4643 7 2.4857 7 2.5559 7 2.526 7 2.501 7 2.4805

216

Change in velocity 4.5186 4.5998 4.5489 4.6607 4.5929 4.555 4.4979 4.554 4.6144 4.4985 4.5751 4.5136 4.5647 4.6051 4.4998 4.5536 4.4666 4.5671 4.5749 4.5198 4.5358 4.4968 4.4863 4.4774 4.5377 4.4909 4.5074 4.5753 4.4492 4.4932 4.5357 4.5143 4.4441 4.474 4.499 4.5195

Meander Change in cluster velocity Amplitude Wavelength Asymmetric factor Starting Velocity Ending velocity S-ASYM-01 200 900 -1.2 7 2.4814 4.5186 S-ASYM-02 200 900 -1.1806 7 2.4002 4.5998 S-ASYM-03 200 900 -1.1612 7 2.4511 4.5489 S-ASYM-04 200 900 -1.1418 7 2.3393 4.6607 S-ASYM-05 200 900 -1.1224 7 2.4071 4.5929 S-ASYM-06 200 900 -1.1031 7 2.445 4.555 S-ASYM-07 200 900 -1.0837 7 2.5021 4.4979 S-ASYM-08 200 900 -1.0643 7 2.446 4.554 S-ASYM-09 200 900 -1.0449 7 2.3856 4.6144 S-ASYM-10 200 900 -1.0255 7 2.5015 4.4985 S-ASYM-11 200 900 -1.0061 7 2.4249 4.5751 S-ASYM-12 200 900 -0.9867 7 2.4864 4.5136 S-ASYM-13 200 900 -0.9673 7 2.4353 4.5647 S-ASYM-14 200 900 -0.948 7 2.3949 4.6051 S-ASYM-15 200 900 -0.9286 7 2.5002 4.4998 S-ASYM-16 200 900 -0.9092 7 2.4464 4.5536 S-ASYM-17 200 900 -0.8898 7 2.5334 4.4666 S-ASYM-18 200 900 -0.8704 7 2.4329 4.5671 S-ASYM-19 200 900 -0.851 7 2.4251 4.5749 S-ASYM-20 200 900 -0.8316 7 2.4802 4.5198 S-ASYM-21 200 900 -0.8122 7 2.4642 4.5358 S-ASYM-22 200 900 -0.7929 7 2.5032 4.4968 S-ASYM-23 200 900 -0.7735 7 2.5137 4.4863 S-ASYM-24 200 900 -0.7541 7 2.5226 4.4774 S-ASYM-25 200 900 -0.7347 7 2.4623 4.5377 S-ASYM-26 200 900 -0.7153 7 2.5091 4.4909 S-ASYM-27 200 900 -0.6959 7 2.4926 4.5074 S-ASYM-28 200 900 -0.6765 7 2.4247 4.5753 S-ASYM-29 200 900 -0.6571 7 2.5508 4.4492 S-ASYM-30 200 900 -0.6378 7 2.5068 4.4932 S-ASYM-31 200 900 -0.6184 7 2.4643 4.5357 S-ASYM-32 200 900 -0.599 7 2.4857 4.5143 S-ASYM-33 200 900 -0.5796 7 2.5559 4.4441 S-ASYM-34 200 900 -0.5602 7 2.526 4.474 S-ASYM-35 200 900 -0.5408 7 2.501 4.499 S-ASYM-36 200 900 -0.5214 7 2.4805 4.5195 S-ASYM-37 200 900 -0.502 7 2.4933 4.5067 S-ASYM-38 200 900 -0.4827 7 2.4793 4.5207 S-ASYM-39 200 900 -0.4633 7 2.4601 4.5399 S-ASYM-40 200 900 -0.4439 7 2.4715 4.5285 S-ASYM-41 200 900 -0.4245 7 2.5283 4.4717 S-ASYM-42 200 900 -0.4051 7 2.4901 4.5099 S-ASYM-43 200 900 -0.3857 7 2.5841 4.4159 S-ASYM-44 200 900 -0.3663 7 2.455 4.545 S-ASYM-45 200 900 -0.3469 7 2.4712 4.5288 S-ASYM-46 200 900 -0.3276 7 2.4489 4.5511 S-ASYM-47 200 900 -0.3082 7 2.4925 4.5075 S-ASYM-48 200 900 -0.2888 7 2.4005 4.5995 S-ASYM-49 200 900 -0.2694 7 2.4516 4.5484 S-ASYM-50 200 900 -0.25 7 2.4394 4.5606 S-WAVELEN200 500 -0.1 7 2.4774 4.5226 S-WAVELEN200 510.2 -0.1 7 2.4021 4.5979 S-WAVELEN200 520.41 -0.1 7 2.427 4.573 S-WAVELEN200 530.61 -0.1 7 2.4325 4.5675 S-WAVELEN200 540.82 -0.1 7 2.4814 4.5186 assymetric factorvelocity S-WAVELEN200 551.02 -0.1 7 2.48354.7 4.5165 S-WAVELEN200 561.22 -0.1 7 2.441 4.559 4.65 S-WAVELEN200 571.43 -0.1 7 2.4555 4.5445 S-WAVELEN200 581.63 -0.1 7 2.4189 4.5811 S-WAVELEN200 591.84 -0.1 7 2.43294.6 4.5671 S-WAVELEN200 602.04 -0.1 7 2.4124 4.5876 4.55 S-WAVELEN200 612.24 -0.1 7 2.4151 4.5849 S-WAVELEN200 622.45 -0.1 7 2.3964 4.6036 S-WAVELEN200 632.65 -0.1 7 2.45094.5 4.5491 S-WAVELEN200 642.86 -0.1 7 2.408 4.592 S-WAVELEN200 653.06 -0.1 7 2.3846 4.6154 4.45 S-WAVELEN200 663.27 -0.1 7 2.498 4.502 S-WAVELEN200 673.47 -0.1 7 2.48884.4 4.5112 -1.2 -1 -0.8 -0.4 -0.2 S-WAVELEN- -1.4 200 683.67 -0.1-0.6 7 2.4316 0 4.5684 S-WAVELEN200 693.88 -0.1 7 2.3582 4.6418 S-WAVELEN200 704.08 -0.1 7 2.3261 4.6739 S-WAVELEN- Figure 8.8200 -0.1 for Machine-Learning 7 2.4293 4.5707 Asymmetry714.29 Factor–Velocity Dataset Training S-WAVELEN200 724.49 -0.1 7 2.3467 4.6533 Wavelength--0.1 velocity S-WAVELEN- 4.8 200 734.69 7 2.3825 4.6175 S-WAVELEN200 744.9 -0.1 7 2.3839 4.6161 S-WAVELEN200 755.1 -0.1 7 2.3651 4.6349 S-WAVELEN- 4.75 200 765.31 -0.1 7 2.4275 4.5725 S-WAVELEN200 775.51 -0.1 7 2.3543 4.6457 S-WAVELEN- 4.7 200 785.71 -0.1 7 2.331 4.669 S-WAVELEN200 795.92 -0.1 7 2.3936 4.6064 S-WAVELEN200 806.12 -0.1 7 2.3588 4.6412 4.65 S-WAVELEN200 816.33 -0.1 7 2.3193 4.6807 S-WAVELEN200 826.53 -0.1 7 2.3632 4.6368

Wavelength–Velocity Training Dataset

-1.4

4.8

4.75

4.7

4.65

Meander cluster S-ASYM-01 S-ASYM-02 S-ASYM-03 S-ASYM-04 S-ASYM-05 S-ASYM-06 S-ASYM-07 S-ASYM-08 S-ASYM-09 S-ASYM-10 S-ASYM-11 S-ASYM-12 S-ASYM-13 S-ASYM-14 S-ASYM-15 S-ASYM-16 S-ASYM-17 S-ASYM-18 S-ASYM-19 S-ASYM-20 S-ASYM-21 S-ASYM-22 S-ASYM-23 S-ASYM-24 S-ASYM-25 S-ASYM-26 S-ASYM-27 S-ASYM-28 S-ASYM-29 S-ASYM-30 S-ASYM-31 S-ASYM-32 S-ASYM-33 S-ASYM-34 S-ASYM-35 S-ASYM-36 S-ASYM-37 S-ASYM-38 S-ASYM-39 S-ASYM-40 S-ASYM-41 S-ASYM-42 S-ASYM-43 S-ASYM-44 S-ASYM-45 S-ASYM-46 S-ASYM-47 S-ASYM-48 S-ASYM-49 S-ASYM-50 S-WAVELENS-WAVELENS-WAVELENS-WAVELENS-WAVELENS-WAVELENS-WAVELENS-WAVELENS-WAVELENS-WAVELENS-WAVELENS-WAVELENS-WAVELENS-WAVELENS-WAVELENS-WAVELENS-WAVELENS-WAVELENS-WAVELEN-

4.6

Amplitude

4.55

4.5

4.45

200 200 200 200 200 200 200 200 200 200 200 200 0 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200

-1.2

Appendix

Asymmetry Factor–Velocity Training Dataset

S-ASYM-36 200 900 -0.5214 7 2.4805 4.5195 4.6 S-ASYM-37 200 900 -0.502 7 2.4933 4.5067 S-ASYM-38 200 900 -0.4827 7 2.4793 4.5207 S-ASYM-39 200 900 -0.4633 7 2.4601 4.5399 4.55 S-ASYM-40 200 900 -0.4439 7 2.4715 4.5285 S-ASYM-41 200 900 -0.4245 7 2.5283 4.4717 S-ASYM-42 200 900 -0.4051 7 2.4901 4.5099 4.5 S-ASYM-43 200 900 -0.3857 7 2.5841 4.4159 S-ASYM-44 200 900 -0.3663 7 2.455 4.545 S-ASYM-45 200 900 -0.3469 7 2.4712 4.5288 4.45 S-ASYM-46 200 900 -0.3276 7 2.4489 4.5511 0 S-ASYM-47 200 900 -0.3082 7 2.4925 4.5075 S-ASYM-48 200 900 -0.2888 7 2.4005 4.5995 Meander Change4.5484 in S-ASYM-49 200 900 -0.2694 7 2.4516 cluster velocity Amplitude 200 Wavelength S-ASYM-50 900 Asymmetric factor -0.25 Starting Velocity7 Ending velocity 2.4394 4.5606 S-ASYM-01 900 -1.2 2.4814 4.5186 S-WAVELEN200 500 -0.1 7 2.4774 4.5226 assymetric factorvelocity S-ASYM-02 900 4.7 -1.1806 2.4002 4.5998 S-WAVELEN200 510.2 -0.1 7 2.4021 4.5979 S-ASYM-03 900 -1.1612 2.4511 4.5489 S-WAVELEN200 520.41 -0.1 7 2.427 4.573 S-ASYM-04 900 -1.1418 2.3393 4.6607 S-WAVELEN200 530.61 -0.1 7 2.4325 4.5675 4.65 S-ASYM-05 900 -1.1224 2.4071 4.5929 S-WAVELEN200 540.82 -0.1 7 2.4814 4.5186 S-ASYM-06 900 -1.1031 2.445 4.555 S-WAVELEN200 551.02 -0.1 7 2.4835 4.5165 S-ASYM-07 900 4.6 -1.0837 2.5021 4.4979 S-WAVELEN200 561.22 -0.1 7 2.441 4.559 S-ASYM-08 900 -1.0643 2.446 4.554 S-WAVELEN200 571.43 -0.1 7 2.4555 4.5445 S-ASYM-09 900 -1.0449 2.3856 4.6144 S-WAVELEN200 581.63 -0.1 7 2.4189 4.5811 S-ASYM-10 900 4.55 -1.0255 2.5015 4.4985 S-WAVELEN200 591.84 -0.1 7 2.4329 4.5671 S-ASYM-11 200 900 -1.0061 7 2.4249 4.5751 S-WAVELEN602.04 -0.1 2.4124 4.5876 S-ASYM-12 900 4.5 -0.9867 2.4864 4.5136 S-WAVELEN200 612.24 -0.1 7 2.4151 4.5849 S-ASYM-13 900 -0.9673 2.4353 4.5647 S-WAVELEN200 622.45 -0.1 7 2.3964 4.6036 S-ASYM-14 900 -0.948 2.3949 4.6051 S-WAVELEN200 632.65 -0.1 7 2.4509 4.5491 S-ASYM-15 900 4.45 -0.9286 2.5002 4.4998 S-WAVELEN200 642.86 -0.1 7 2.408 4.592 S-ASYM-16 900 -0.9092 2.4464 4.5536 S-WAVELEN200 653.06 -0.1 7 2.3846 4.6154 S-ASYM-17 900 4.4 -0.8898 2.5334 4.4666 S-WAVELEN200 663.27 -0.1 7 2.498 4.502 S-ASYM-18 900 -0.8704 2.4329 4.5671 S-WAVELEN200 -0.1 7 2.4888 4.5112 -1.2 -1 -0.8 -0.6 -0.4 -0.2 673.47 0 -1.4 S-ASYM-19 900 -0.851 2.4251 4.5749 S-WAVELEN200 683.67 -0.1 7 2.4316 4.5684 S-ASYM-20 200 900 -0.8316 7 2.4802 4.5198 S-WAVELEN693.88 -0.1 2.3582 4.6418 S-ASYM-21 900 -0.8122 2.4642 4.5358 S-WAVELEN200 704.08 -0.1 7 2.3261 4.6739 S-ASYM-22 900 -0.7929 2.5032 4.4968 S-WAVELEN200 714.29 -0.1 7 2.4293 4.5707 S-ASYM-23 900 -0.7735 2.5137 4.4863 S-WAVELEN200 724.49 -0.1 7 2.3467 4.6533 S-ASYM-24 900 -0.7541 2.5226 4.4774 200 734.69 -0.1 7 2.3825 4.6175 Wavelength-S-WAVELENvelocity S-ASYM-25 900 -0.7347 2.4623 4.5377 S-WAVELEN200 744.9 -0.1 7 2.3839 4.6161 4.8 S-ASYM-26 900 -0.7153 2.5091 4.4909 S-WAVELEN200 755.1 -0.1 7 2.3651 4.6349 S-ASYM-27 900 -0.6959 2.4926 4.5074 S-WAVELEN200 765.31 -0.1 7 2.4275 4.5725 S-ASYM-28 900 -0.6765 2.4247 4.5753 S-WAVELEN200 775.51 -0.1 7 2.3543 4.6457 4.75 S-ASYM-29 900 -0.6571 2.5508 4.4492 S-WAVELEN200 785.71 -0.1 7 2.331 4.669 S-ASYM-30 900 -0.6378 2.5068 4.4932 S-WAVELEN200 795.92 -0.1 7 2.3936 4.6064 4.7 S-ASYM-31 200 900 -0.6184 7 2.4643 4.5357 S-WAVELEN806.12 -0.1 2.3588 4.6412 S-ASYM-32 900 -0.599 2.4857 4.5143 S-WAVELEN200 816.33 -0.1 7 2.3193 4.6807 S-ASYM-33 900 -0.5796 2.5559 4.4441 S-WAVELEN200 826.53 -0.1 7 2.3632 4.6368 4.65 S-ASYM-34 900 -0.5602 2.526 4.474 S-WAVELEN200 836.73 -0.1 7 2.3418 4.6582 S-ASYM-35 900 -0.5408 2.501 4.499 S-WAVELEN200 846.94 -0.1 7 2.3407 4.6593 S-ASYM-36 900 -0.5214 2.4805 4.5195 S-WAVELEN200 857.14 -0.1 7 2.3806 4.6194 Change in 4.6 S-ASYM-37 900 -0.502 2.4933 4.5067 S-WAVELEN200 867.35 -0.1 7 2.3711 4.6289 velocity Wavelength Asymmetric factor Starting Velocity Ending velocity 900 -1.2 7 2.4814 4.5186 S-ASYM-38 900 -0.4827 2.4793 4.5207 S-WAVELEN200 877.55 -0.1 7 2.3308 4.6692 assymetric factorvelocity 4.7 900 -1.1806 7 2.4002 4.5998 S-ASYM-39 900 -0.4633 2.4601 4.5399 S-WAVELEN200 887.76 -0.1 7 2.3632 4.6368 900 -1.1612 7 2.4511 4.5489 4.55 900 -1.1418 7 2.3393 4.6607 S-ASYM-40 900 -0.4439 2.4715 4.5285 S-WAVELEN200 897.96 -0.1 7 2.3053 4.6947 4.65 900 -1.1224 7 2.4071 4.5929 S-ASYM-41 900 -0.4245 2.5283 4.4717 S-WAVELEN200 908.16 -0.1 7 2.3349 4.6651 900 -1.1031 7 2.445 4.555 900 -1.0837 7 2.5021 4.4979 S-ASYM-42 900 -0.4051 2.4901 4.5099 4.6 2.3484 S-WAVELEN200 918.37 -0.1 7 4.6516 4.5 900 -1.0643 7 2.446 4.554 S-ASYM-43 900 -0.3857 2.5841 4.4159 S-WAVELEN200 928.57 -0.1 7 2.2876 4.7124 900 -1.0449 7 2.3856 4.6144 4.55 900 -1.0255 7 2.5015 4.4985 S-ASYM-44 900 -0.3663 2.455 4.545 S-WAVELEN200 938.78 -0.1 7 2.2724 4.7276 900 -1.0061 7 2.4249 4.5751 S-ASYM-45 900 -0.3469 2.4712 4.5288 S-WAVELEN200 948.98 -0.1 7 2.2784 4.7216 900 -0.9867 7 2.4864 4.5136 4.45 4.5 900 -0.9673 7 2.4353 4.5647 S-ASYM-46 900 1200 -0.3276 2.4489 4.5511 S-WAVELEN200 1000 959.18 -0.1 7 2.303 4.697 200 400 600 800 0 900 -0.948 7 2.3949 4.6051 S-ASYM-47 900 -0.3082 2.4925 4.5075 S-WAVELEN200 969.39 -0.1 7 4.7805 4.45 2.2195 900 -0.9286 7 2.5002 4.4998 900 -0.9092 7 2.4464 4.5536 S-ASYM-48 900 -0.2888 2.4005 4.5995 S-WAVELEN200 979.59 -0.1 7 2.2822 4.7178 900 -0.8898 7 2.5334 4.4666 4.4 2.2427 S-ASYM-49 900 -0.2694 2.4516 4.5484 S-WAVELEN200 989.8 -0.1 7 4.7573 900 -0.8704 7 2.4329 4.5671 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 900 -0.851 7 2.4251 4.5749 S-ASYM-50 900 -0.25 2.4394 4.5606 S-WAVELEN200 1000 -0.1 7 2.2785 4.7215 900 -0.8316 7 2.4802 4.5198 S-WAVELEN200 500 -0.1 7 2.4774 4.5226 900 -0.8122 7 2.4642 4.5358 900 -0.7929 7 2.5032 4.4968 S-WAVELEN200 510.2 -0.1 7 2.4021 4.5979 900 -0.7735 7 2.5137 4.4863 S-WAVELEN200 520.41 -0.1 7 2.427 4.573 900 -0.7541 7 2.5226 4.4774 Wavelength- velocity 900 -0.7347 7 2.4623 4.5377 S-WAVELEN200 530.61 -0.1 7 2.4325 4.5675 4.8 900 -0.7153 7 2.5091 4.4909 S-WAVELEN200 540.82 -0.1 7 2.4814 4.5186 900 -0.6959 7 2.4926 4.5074 900 -0.6765 7 2.4247 4.5753 S-WAVELEN200 551.02 -0.1 7 2.4835 4.5165 4.75 900 -0.6571 7 2.5508 4.4492 S-WAVELEN200 561.22 -0.1 7 2.441 4.559 900 -0.6378 7 2.5068 4.4932 4.7 900 -0.6184 7 2.4643 4.5357 S-WAVELEN200 571.43 -0.1 7 2.4555 4.5445 900 -0.599 7 2.4857 4.5143 S-WAVELEN200 581.63 -0.1 7 2.4189 4.5811 900 -0.5796 7 2.5559 4.4441 4.65 900 -0.5602 7 2.526 4.474 S-WAVELEN200 591.84 -0.1 7 2.4329 4.5671 900 -0.5408 7 2.501 4.499 S-WAVELEN200 602.04 -0.1 7 2.4124 4.5876 900 -0.5214 7 2.4805 4.5195 4.6 S-WAVELEN200 612.24 -0.1 7 2.4151 4.5849 900 -0.502 7 2.4933 4.5067 900 -0.4827 7 2.4793 4.5207 S-WAVELEN200 622.45 -0.1 7 2.3964 4.6036 900 -0.4633 7 2.4601 4.5399 4.55 S-WAVELEN200 632.65 -0.1 7 2.4509 4.5491 900 -0.4439 7 2.4715 4.5285 900 -0.4245 7 2.5283 4.4717 S-WAVELEN200 642.86 -0.1 7 2.408 4.592 900 -0.4051 7 2.4901 4.5099 4.5 S-WAVELEN200 653.06 -0.1 7 2.3846 4.6154 900 -0.3857 7 2.5841 4.4159 900 -0.3663 7 2.455 4.545 S-WAVELEN200 663.27 -0.1 7 2.498 4.502 900 -0.3469 7 2.4712 4.5288 4.45 S-WAVELEN673.47 400 7 2.4888 4.5112 900 -0.3276 7 2.4489 4.5511 0 200 200 600 -0.1 800 1000 1200 900 -0.3082 7 2.4925 4.5075 S-WAVELEN200 683.67 -0.1 7 2.4316 4.5684 900 -0.2888 7 2.4005 4.5995 S-WAVELEN200 693.88 -0.1 7 2.3582 4.6418 900 -0.2694 7 2.4516 4.5484 900 -0.25 7 2.4394 4.5606 S-WAVELEN704.08 -0.1for Machine-Learning 7 2.3261 4.6739 Figure200 8.9 Wavelength–Velocity Dataset Training 500 -0.1 7 2.4774 4.5226 S-WAVELEN200 714.29 -0.1 7 2.4293 4.5707 510.2 -0.1 7 2.4021 4.5979 520.41 -0.1 7 2.427 4.573 S-WAVELEN200 724.49 -0.1 7 2.3467 4.6533 530.61 -0.1 7 2.4325 4.5675 of the semicircular 200 channel section, varied 7diversion 2.3825 angle, channel width, asymmetry factor S-WAVELEN734.69 parametric simulations -0.1 4.6175 540.82 -0.1 Following 7 the selection 2.4814 4.5186 551.02 -0.1 7 2.4835 4.5165 200flow velocity. 744.9 Together, these -0.1 datasets provide 7 the training 2.3839basis for 4.6161 561.22 -0.1 and wavelength 7 2.441 4.559 whileS-WAVELENrecording the resulting a machine-learning model S-WAVELEN200 755.1 -0.1 7 2.3651 4.6349 571.43 -0.1 7 2.4555 4.5445 581.63 -0.1 that learns 7 the 2.4189 4.5811between channel geometry and hydraulic performance, enabling rapid velocity prediction for subsequent channel relationship S-WAVELEN200 765.31 -0.1 7 2.4275 4.5725 591.84 -0.1 7 2.4329 4.5671 S-WAVELEN200 775.51 -0.1 7 2.3543 4.6457 602.04 -0.1 configurations. 7 2.4124 4.5876 217 612.24 -0.1 7 2.4151 4.5849 S-WAVELEN200 785.71 -0.1 7 2.331 4.669 622.45 -0.1 7 2.3964 4.6036 S-WAVELEN200 795.92 -0.1 7 2.3936 4.6064 632.65 -0.1 7 2.4509 4.5491 642.86 -0.1 7 2.408 4.592 S-WAVELEN200 806.12 -0.1 7 2.3588 4.6412 653.06 -0.1 7 2.3846 4.6154 S-WAVELEN200 816.33 -0.1 7 2.3193 4.6807 663.27 -0.1 7 2.498 4.502 673.47 -0.1 7 2.4888 4.5112 S-WAVELEN200 826.53 -0.1 7 2.3632 4.6368 683.67 -0.1 7 2.4316 4.5684

Heatmap

Input

Custom C# Scripts-branching channel

Terrain Rivers

Defines the propagation distance per step

Initial discharge assigned to each seed

Initial velocity assigned to each seed

public enum State { Growing, Blocked, Dissipated, Merged }

Minimum velocity threshold controlling channel dissipation

public class Settings { public double StepLength, TargetVelocity, SplitVelocityLoss; public double SplitThreshold, SplitSlopeLimit, MinimumSpacing; public double SplitGain, SplitSlopeDecay; public double WidthScale, WidthExponent, MinimumWidth; public double DepthScale, DepthExponent, MinimumDepth; public int MaximumSteps; }

A SECTOR_DEG

Appendix

Controls the accumulation rate of branching potential Half-width of the directional search sectortested

Growing

The branch remains active

Blocked

No spatially valid continuation is available

Dissipated

Velocity has fallen below the target threshold

Merged

The branch has connected to an existing river

CurvesStep

Segments generated during each iteration

Widths

Calculated width of each branch

Depths

Calculated depth of each branch

public class Agent { public Point3d Position; public Vector3d Direction; public double Q, V, Width, Depth, SplitPotential; public State Status = State.Growing; public List<Point3d> Path = new List<Point3d>(); } public class Result { public List<PolylineCurve> ChannelPaths = new List<PolylineCurve>(); public List<double> ChannelWidths = new List<double>(); public List<double> ChannelDepths = new List<double>(); public List<string> ChannelStates = new List<string>(); } class Candidate { public Point3d Position, TerrainPoint; public double ElevationChange, Alignment; } Mesh terrain; Curve boundary; List<Curve> rivers; List<Point3d> slopePoints; List<double> slopeValues; Settings settings; readonly List<Agent> agents = new List<Agent>();

The script converts terrain and hydrological inputs into an adaptive branching channel network. Each seed operates as an agent carrying discharge, velocity, width and depth. During each iteration, the agent evaluates potential routes according to terrain slope, directional alignment, site boundaries and channel spacing, before continuing, branching, merging with an existing river, becoming blocked or dissipating below the target velocity. The resulting outputs record the evolving channel paths and dimensions, allowing the network to respond dynamically to local hydraulic and topographic conditions.

218

for (int i = 0; i < seeds.Count; i++) { Agent agent = new Agent { Position = Flat(seeds[i]), Direction = UnitXY(directions[i]), Q = initialDischarges[i], V = initialVelocities[i] };

// Initialise agents and run the propagation sequence public Result Generate( Mesh terrainMesh, Curve siteBoundary, List<Curve> existingRivers, List<Point3d> seeds, List<Vector3d> directions, List<double> initialDischarges, List<double> initialVelocities, List<Point3d> terrainSlopePoints, List<double> terrainSlopeValues, Settings parameters) { terrain = terrainMesh;

}

UpdateDimensions(agent); agent.Path.Add(agent.Position); agents.Add(agent);

for (int step = 0; step < settings.MaximumSteps; step++) { List<Agent> active = agents .Where(a => a.Status == State.Growing).ToList();

}

if (active.Count == 0) break; foreach (Agent agent in active) Propagate(agent);

return BuildResult();

Appendix

Output

Existing drainage lines used as terminal connections

boundary = siteBoundary; rivers = existingRivers; slopePoints = terrainSlopePoints; slopeValues = terrainSlopeValues; settings = parameters;

public class ChannelPropagationCore { // Hydraulic and geometric constants const double G = 9.81; const double Roughness = 0.028; const double BranchAngle = 80.0; const double RiverTolerance = 10.0;

V_Target

Agent States

Provides terrain elevation for candidate evaluation and curve projection

using System; using System.Collections.Generic; using System.Linq; using Rhino; using Rhino.Geometry; using Rhino.Geometry.Intersect;

// Advance one agent through movement, termination and branching void Propagate(Agent agent) { Point3d start = agent.Position; Point3d terrainStart = Project(start); double slope = SlopeAt(terrainStart); Candidate mother, child; bool branching = TryBranch(agent, slope, out mother, out child); if (!branching) { List<Candidate> candidates = Candidates(agent); if (candidates.Count == 0) { agent.Status = State.Blocked; return; }

}

mother = candidates .OrderBy(c => Category(c.ElevationChange)) .ThenBy(c => Math.Abs(c.ElevationChange)) .ThenByDescending(c => c.Alignment).First();

Point3d end = mother.Position; double velocityBeforeMove = agent.V; if (ConnectToRiver(start, ref end)) { branching = false; agent.Status = State.Merged; } Move(agent, start, end, terrainStart.Z, slope); if (branching) agent.SplitPotential = 0.0; if (agent.Status == State.Growing && agent.V <= settings.TargetVelocity) agent.Status = State.Dissipated;

219

}

if (branching) Split(agent, start, child, terrainStart.Z, velocityBeforeMove);

// Generate terrain-responsive candidates within a 140-degree sector List<Candidate> Candidates(Agent agent) { var candidates = new List<Candidate>(); Point3d start = Project(agent.Position); for (double angle = -70.0; angle <= 70.0; angle += 10.0) { Vector3d direction = Rotate(agent.Direction, angle); Point3d position = Flat( agent.Position + direction * settings.StepLength); if (!SpatiallyValid(position, agent.Width)) continue; Point3d terrainPoint = Project(position);

}

Appendix

return candidates;

// Test branching potential, slope and available side positions bool TryBranch( Agent agent, double slope, out Candidate mother, out Candidate child) { mother = null; child = null; if (agent.SplitPotential < settings.SplitThreshold || slope >= settings.SplitSlopeLimit) return false; mother = CandidateAt(agent, 0.0); child = new[] { CandidateAt(agent, BranchAngle), CandidateAt(agent, -BranchAngle) } .Where(c => c != null) .OrderBy(c => c.TerrainPoint.Z) .FirstOrDefault(); if (mother == null || child == null) return false; agent.SplitPotential = 0.0; return true; } // Create one candidate at a specified angle Candidate CandidateAt(Agent agent, double angle) { Vector3d direction = Rotate(agent.Direction, angle); Point3d position = Flat( agent.Position + direction * settings.StepLength); if (!SpatiallyValid(position, agent.Width)) return null;

220

Point3d start = Project(agent.Position); Point3d end = Project(position); return new Candidate { Position = position, TerrainPoint = end,

};

// Update position, velocity, direction and branching potential void Move( Agent agent, Point3d start, Point3d end, double startZ, double slope) { agent.V = Velocity(agent.V, startZ - Project(end).Z); agent.Direction = UnitXY(end - start); agent.Position = end; agent.Path.Add(end); agent.SplitPotential += settings.StepLength * settings.SplitGain * Math.Exp(-settings.SplitSlopeDecay * slope); }

// Enforce the site boundary and minimum channel separation bool SpatiallyValid(Point3d point, double width) { if (boundary.Contains(point, Plane.WorldXY, 0.001) != PointContainment.Inside) return false;

}

child.Path.Add(origin); child.Path.Add(target.Position); UpdateDimensions(mother); UpdateDimensions(child); Point3d end = child.Position; if (ConnectToRiver(origin, ref end)) child.Status = State.Merged; else if (child.V <= settings.TargetVelocity) child.Status = State.Dissipated; child.Position = end; child.Path[child.Path.Count - 1] = end; agents.Add(child);

// Apply gravitational acceleration and simplified roughness loss double Velocity(double velocity, double deltaZ) { return Math.Sqrt(Math.Max( 0.0, velocity * velocity + 2.0 * G * deltaZ)) - Roughness * settings.StepLength; }

static int Category(double deltaZ) { if (Math.Abs(deltaZ) < 1e-6) return 0; return deltaZ > 0.0 ? 1 : 2; }

return !agents.Any(a => a.Path.Skip(1) .Take(Math.Max(0, a.Path.Count - 3)) .Any(p => p.DistanceTo(point) < settings.MinimumSpacing + 0.5 * (a.Width + width)));

// Convert completed agents into appendix-ready outputs Result BuildResult() { Result result = new Result();

// Terminate a branch at an intersected or nearby river bool ConnectToRiver(Point3d start, ref Point3d end) { LineCurve path = new LineCurve(start, end);

foreach (Agent agent in agents) { if (agent.Path.Count < 2) continue;

foreach (Curve river in rivers) { var intersections = Intersection.CurveCurve( path, river, 0.001, 0.001);

// Divide discharge and create the secondary branch void Split( Agent mother, Point3d origin, Candidate target, double startZ, double velocityBeforeMove) { double cosine = Math.Cos(RhinoMath.ToRadians(BranchAngle)); double originalQ = mother.Q; mother.Q = originalQ / (1.0 + cosine); Agent child = new Agent { Position = target.Position, Direction = UnitXY(target.Position - origin), Q = originalQ * cosine / (1.0 + cosine), V = Velocity(velocityBeforeMove, startZ - target.TerrainPoint.Z) * (1.0 - settings.SplitVelocityLoss * (1.0 - cosine)) };

}

} }

.OrderBy(i => slopePoints[i].DistanceToSquared(point)).First(); return slopeValues[index];

result.ChannelPaths.Add( new PolylineCurve(agent.Path));

if (intersections.Count > 0) { end = intersections[0].PointA; return true; }

result.ChannelWidths.Add( agent.Width);

double t; river.ClosestPoint(end, out t); Point3d closest = river.PointAt(t);

result.ChannelStates.Add( agent.Status.ToString());

result.ChannelDepths.Add( agent.Depth);

}

if (end.DistanceTo(closest) < RiverTolerance) { end = closest; return true; }

}

return result;

static Point3d Flat(Point3d point) { return new Point3d(point.X, point.Y, 0.0); }

return false;

static Vector3d UnitXY(Vector3d vector) { vector.Z = 0.0; vector.Unitize(); return vector; }

// Project an XY point vertically onto the terrain mesh Point3d Project(Point3d point) { var ray = new Ray3d( new Point3d(point.X, point.Y, 0.0), Vector3d.ZAxis); return ray.PointAt(Intersection.MeshRay(terrain, ray)); } // Retrieve the nearest sampled terrain-slope value double SlopeAt(Point3d point) { int index = Enumerable.Range(0, slopePoints.Count)

Appendix

}

candidates.Add(new Candidate { Position = position, TerrainPoint = terrainPoint, ElevationChange = start.Z - terrainPoint.Z, Alignment = direction * agent.Direction });

}

ElevationChange = start.Z - end.Z, Alignment = direction * agent.Direction

}

static Vector3d Rotate(Vector3d vector, double degrees) { double angle = RhinoMath.ToRadians(degrees); return new Vector3d( vector.X * Math.Cos(angle) - vector.Y * Math.Sin(angle), vector.X * Math.Sin(angle) + vector.Y * Math.Cos(angle), 0.0); }

// Derive channel width and depth from discharge void UpdateDimensions(Agent agent) { agent.Width = Math.Max(settings.MinimumWidth, settings.WidthScale * Math.Pow(agent.Q, settings.WidthExponent)); agent.Depth = Math.Max(settings.MinimumDepth, settings.DepthScale * Math.Pow(agent.Q, settings.DepthExponent)); }

221

Cellular Automata Pre-CA Environmental Suitability Mapping

Cellular Automata Simulation Outcome LEGEND

10.00

| Permanent Death=Eliminate

| 742 cells (7.4 %)

9.00

| Initial state

| 140 cells (1.4 %)

8.00

| Water collection nodes

| 18 cells (0.2 %)

7.00

| Agro-production

| 4065 cells (40.7 %)

6.00

| Utility

| 184 cells (1.8 %)

5.00

| Residence

| 1386 cells (13.9 %)

4.00

3.00

| Institution

| 184 cells (1.8 %)

2.00

| Research and Production

| 84 cells (0.8 %)

1.00

0.00

fault lines

channel

geological aquifer

| Social-Community Recreation | 231 cells (2.3 %) | Bio-Remediation Forest

| 2782 cells (27.8 %)

| Mobility

| 184 cells (1.8 %)

Water collection nodes

10.00

9.00

8.00

7.00

6.00

5.00

4.00

3.00

2.00

1.00

0.00

precipitation

new_nature_path_01

road-slope

Appendix

Water channel

Emerging islands

Function pockets Dense distribution

Based on region environment

sun

10.00

9.00

8.00

7.00

6.00

5.00

4.00

3.00

2.00

1.00

0.00

unbuildable area

wind

Before the cellular automata simulation, the site is translated into a series of normalised environ- Figure 8.10 Environmental Heatmaps Used mental heatmaps. Channel proximity, fault lines, aquifer conditions, nature paths, precipitation, for Pre-CA Suitability Evaluation road slope, solar exposure, unbuildable areas and wind are converted into comparable cell-based values. These layers establish the environmental suitability of each location and provide the spatial input for subsequent functional allocation.

222

Figure 8.11 CA-Generated Functional Distribution, reproduced from Figure 5.16

The cellular automata model combines the environmental suitability values with neighbour compatibility, density thresholds, functional capacities and water-collection seeds. During each iteration, competing functions bid for available cells, while weaker allocations may be replaced by more suitable states. The resulting distribution forms clustered functional pockets that respond simultaneously to environmental conditions, water demand and adjacency relationships.

223

Appendix

CA Programme Schedule

Figure 8.12 CA Programme Schedule: Capacity, Land Allocation, Spatial Conditions

224

The programme schedule translates the design brief into quantitative inputs for the cellular automata model. Each function is assigned a population or operational capacity, a target land area, a percentage of the overall site and a primary spatial condition. These values establish the capacity limits and location preferences used during the bidding process, allowing residential, productive, ecological and infrastructural functions to be allocated according to both programme requirements and site conditions. 225

Residence

Environmental Factor Weights

Institution

Cellular Automata Environmental Factor Weights Signed Heatmap coefficients used to calculate the environmental body score of each function

Nature Path

Unbuildable Area

-10

Sun

-5

Wind

-5

Road Slope

Channel

-3

Geological Aquifer

Fault Lines

Precipitation

Nature Path

Unbuildable Area

-10

Sun

-5

Wind

-5

Road Slope Cellular Automata Environmental Factor Weights

Neighbour Compatibility Rules

Channel -10 Signed Heatmap coefficients used to calculate the environmental body score of each function Individual Function

Environmental Factor

Geological Aquifer

Score Individual Function

Agro-Production

Precipitation

Nature Path

Appendix

Institution

Precipitation Precipitation

-5 5

Nature Nature Path Path

-5 10

Unbuildable Area Unbuildable Area

10 0 -5 5 -5 0 2 5 10 10 -10

Sun

Wind

Road Slope

Channel

Geological Aquifer

Sun Sun Wind Wind Road Slope Road Slope Channel Channel Geological Aquifer

Fault Lines

-6

Geological Aquifer Fault Lines

0 10

Cellular Automata Neighbour Compatibility Rules

Precipitation

Fault Lines Precipitation

-6 0

Heatmap-driven functional allocation: neighbour weights, state definitions and density conditions

Nature Path

Precipitation Nature Path

0 -8

Unbuildable Nature Path Area

Unbuildable Area

Sun

Research and Propagation Agro-Production

Wind

Road Slope

Channel

Geological Aquifer

Fault Lines

Precipitation

Nature Path

Unbuildable Area

-10

Sun

-5

Wind

-5

Road Slope

Channel

-3

Geological Aquifer

Fault Lines

Precipitation

Nature Path

Unbuildable Area

-10

Sun

-5

Wind

Utility Body-score calculation

Channel

-10

Geological Aquifer

Fault Lines

Precipitation

-5

5 0

Wind Sun

-5 0

Road Slope Wind

0 0

Channel Road Slope

Geological Aquifer Channel Body Score = Σ (signed coefficient × Heatmap value) / normalisationFault denominator Lines Geological Aquifer Precipitation

Bio-remediation Forest

Residence

Mobility

-5

Road Slope

Sun Unbuildable Area

Institution

3 5 -10 10 5 0 10

Fault Lines Nature Path

10 -10

Unbuildable PrecipitationArea

0 0

AgroProduction

Utility

Residence

Institution

Research and Propagation

SocioCommunity Recreation

Bioremediation Forest

Mobility

Agro-Production

0.0

0.3

0.7

0.0

0.2

0.5

0.8

1.0

0.0

Utility

0.4

0.0

1.0

0.4

1.0

0.3

Residence

0.7

1.0

0.0

0.3

1.0

0.2

1.5

Institution

1.0

0.7

0.0

1.0

Research and Propagation

1.0

0.5

1.5

1.0

0.0

Individual Function

Body-score calculation

Permanent Death / Eliminated

Density Threshold Within x m

1.0

600

0.8

1.0

0.2

1.5

1.0

300

1.0

0.3

1.5

1.0

0.5

0.0

0.5

1.0

Water Collection Remaining / Node Unassigned

Body Score = Σ (signed coefficient × Heatmap value) / normalisation denominator

Sun Nature Path

5 10

Wind Unbuildable Area

0 -10

Socio-Community Recreation

0.5

1.0

0.2

0.7

1.0

0.0

0.5

1.0

Road Sun Slope

-10 -5

Bio-remediation Forest

0.8

1.0

1.5

1.0

0.8

0.0

1.0

0.0

1.0

Channel Wind

5 -5

No density condition

Mobility

1.0

0.8

0.2

0.5

0.0

1.0

0.0

1.5

1.0

Geological Road SlopeAquifer

-9 8

Fault Lines Channel

-10 -3

Precipitation Geological Aquifer

0 10

Nature Path Fault Lines

10 10

Unbuildable PrecipitationArea

-5 0

Sun Nature Path

0 10

Wind Unbuildable Area

0 -10

Road Sun Slope

10 -5

Channel Wind

-10 -5

Geological Road SlopeAquifer

0 10

Fault Lines Channel

10 -10

Appendix

Residence

10 Score 15

Unbuildable Area

Socio-Community Recreation Utility

Environmental Factor Fault Lines

Scores reproduce the C# neighbour-weight matrix (0.0–1.5). x equals the Grasshopper densityRadius input; the current documented value is 200 m.

Notes: Scores reproduce the C# neighbour-weight matrix (0.0–1.5). x equals the Grasshopper densityRadius input; the current documented value is 200 m

Geological Aquifer 10 The coefficients reproduce the eight body-score equations in the Grasshopper C# script. Negative Fault Lines+ Mmax − M. 15 coefficients indicate use of the reversed value M* = Mmin

Notes: The coefficients reproduce the eight body-score equations in the Grasshopper C# script. Negative coefficients indicate use of thePrecipitation reversed value M* = Mmin + Mmax -5 − M.

Research and Propagation

Nature Path

-5

Unbuildable Area

Sun

-5

Wind

-5

Road Slope

Research and Propagation

Nature Path

-5

Unbuildable Area

Sun

-5

Wind

-5

10 The environmental weight matrixChannel defines how each site condition contributes to the suitability of Figure 8.13 Environmental Factor2Weight Channel Geological Aquifer for CA Functional States 10 individual functions. Positive coefficients favour cells with -10 higher heatmap values, negative coeffi- Matrix Geological Aquifer -10 Lines 10 cients reverse the environmentalFault preference, and zero values exclude the factor from the calculaFault Lines 10 Precipitation 0 tion. The weighted values are combined into a normalised environmental score used during the CA Precipitation 0 -8 bidding and replacement process.Nature Path Nature Path -8 Road Slope

226

Socio-Community Recreation

Unbuildable Area

Sun

Wind

-5

Road Slope

Channel

Geological Aquifer

Unbuildable Area

-10

Socio-Community Recreation

Figure 8.14 Environmental Factor Weight Matrix for CA Functional States

The compatibility matrix defines the preferred spatial relationships between neighbouring functional states. Higher values encourage particular functions to cluster or remain adjacent, while lower values reduce the likelihood of proximity. These scores are evaluated together with the density threshold measured within a 200 m radius, allowing the CA model to balance environmental suitability with functional organisation.

Sun

Wind

-5

Road Slope

Channel

Geological Aquifer

-10

227

Definition - Cellular Automata

Input

Total number of cells within the site

initialState

Initial function assigned to each cell

adjTree8

Immediate neighbourhood of each cell

densityRadius

Radius used to evaluate larger functional clusters

maxCapacity

Maximum number of cells permitted for each function

CostMap

Environmental heatmap values used to calculate functional suitability

cellTree

Cell indices grouped according to their final functions

scoreTree

Environmental body scores corresponding to the allocated cells

The script establishes a rule-based cellular automata model for translating environmental analysis into functional spatial organisation. Each site cell is assigned an initial state and receives a suitability score derived from the weighted environmental heatmaps. During every iteration, unassigned or replaceable cells are evaluated according to environmental performance, immediate neighbour compatibility, functional dependencies, prohibited adjacencies and the density of similar states within a defined radius. Eligible functions then compete for cells through a bidding process. Capacity limits prevent individual programmes from exceeding their required land allocation, while turnover rules remove weaker assignments and allow more suitable functions to replace them. By adjusting the environmental weights, neighbour matrix, density radius, capacities and iteration count, the model can test alternative design scenarios. The final outputs organise cells into functional clusters and provide the numerical scores used to visualise and evaluate the resulting spatial distribution. 228

#region Usings using System; using System.Linq; using System.Collections.Generic; using System.Globalization; using Rhino.Geometry; using Grasshopper.Kernel; using Grasshopper.Kernel.Data; #endregion public class Script_Instance : GH_ScriptInstance { private bool _initialized; private int _n, _turnover, _iteration; private int[] _state, _capacity, _count; private bool[] _locked; private int[][] _adj8, _adjDensity; private double[][] _body; private List<int[]> _history; private const double DensityBonus = 1.5; private readonly double[,] _W = new double[8, 11] { { 0.0, 0.3, 0.7, 0.0, 0.2, 0.5, 0.8, 1.0, 0.0, 1.0, 1.0 }, { 0.4, 0.0, 1.0, 1.0, 0.4, 1.0, 1.0, 0.3, 0.8, 1.0, 1.0 }, { 0.7, 1.0, 0.0, 0.3, 1.0, 0.2, 1.5, 0.2, 1.5, 1.0, 1.0 }, { 1.0, 0.7, 0.0, 0.0, 1.0, 1.0, 1.0, 0.3, 1.5, 1.0, 1.0 }, { 1.0, 0.5, 1.5, 1.0, 0.0, 1.0, 0.5, 0.0, 0.5, 1.0, 1.0 }, { 0.5, 1.0, 0.2, 0.7, 1.0, 0.0, 0.5, 1.0, 1.0, 1.0, 1.0 }, { 0.8, 1.0, 1.5, 1.0, 1.0, 0.8, 0.0, 1.0, 0.0, 1.0, 1.0 }, { 1.0, 0.8, 0.2, 0.5, 0.0, 1.0, 1.0, 0.0, 1.5, 1.0, 1.0 } }; private readonly int[] _dependency = new int[8] { Mask(1, 2, 3, 6), Mask(2, 3, 6, 9), Mask(4, 5, 7, 9), Mask(4, 5, 9), Mask(1, 3, 6, 9), Mask(2, 4, 7, 8), Mask(1, 8), Mask(4, 6, 9) }; private readonly int[] _forbidden = new int[8] { 0, 0, Mask(8, 1), Mask(1), Mask(4), 0, Mask(4), Mask(1) }; private readonly int[] _densityThreshold = { 600, 10, 300, 10, 10, 10, int.MaxValue, 10 }; private struct Candidate { public double Score; public int Cell, StateIndex; public Candidate(double score, int cell, int stateIndex) { Score = score; Cell = cell; StateIndex = stateIndex; } } private void RunScript( bool run, bool reset, int totalCells, List<int> initialState, DataTree<int> adjTree8, List<int> maximumCapacity, int iterations, List<double> precipitationCostMap, List<double> naturePathCostMap, List<double> unbuildableAreaCostMap, List<double> sunCostMap, List<double> windCostMap, List<double> roadSlopeCostMap, List<double> channelCostMap, List<double> geologicalAquiferCostMap, List<double> faultLinesCostMap, List<Point3d> cellCenters, double densityRadius, int turnoverRate, string costMapDomain, ref object cellTree, ref object scoreTree) { if (reset || !_initialized) { Initialize(totalCells, initialState, adjTree8, maximumCapacity, cellCenters, densityRadius, turnoverRate, costMapDomain, precipitationCostMap, naturePathCostMap, unbuildableAreaCostMap, sunCostMap, windCostMap, roadSlopeCostMap, channelCostMap, geologicalAquiferCostMap, faultLinesCostMap); } if (!_initialized) return; int target = Math.Max(0, iterations); if (run) { while (_iteration < target) StepOneRound();

}

if (_iteration > target) RewindTo(target); } EmitOutput(ref cellTree, ref scoreTree);

// Prepare the CA state and convert heatmaps into function scores. private void Initialize( int totalCells, List<int> initialState, DataTree<int> adjTree8, List<int> maximumCapacity, List<Point3d> centers, double densityRadius, int turnoverRate, string domain, List<double> pr, List<double> np, List<double> ub, List<double> sun, List<double> wind, List<double> road, List<double> channel, List<double> aquifer, List<double> fault) { if (totalCells <= 0 || initialState == null || initialState.Count != totalCells || centers == null || centers.Count != totalCells || densityRadius <= 0 || maximumCapacity == null || maximumCapacity.Count < 10 || turnoverRate < 1) { Error(“Invalid CA inputs or list lengths.”); return; } double domainMin, domainMax; if (!ParseDomain(domain, out domainMin, out domainMax)) { Error(“CostMapDomain must use the format: 0 to 10.”); return; } _n = totalCells; _turnover = turnoverRate; _iteration = 0; _state = initialState.ToArray(); _locked = initialState.Select(state => state >= 2).ToArray(); _capacity = maximumCapacity.ToArray(); _history = new List<int[]>(); _adj8 = ReadAdjacency(adjTree8, _n); _adjDensity = BuildDensityAdjacency(centers, densityRadius); _count = new int[8]; foreach (int state in _state) if (state >= 2 && state <= 9) _count[state - 2]++; BuildBodyScores(domainMin, domainMax, pr, np, ub, sun, wind, road, channel, aquifer, fault); _initialized = true;

} // Replace weak allocations and distribute new functions by bidding. private void StepOneRound() { var changes = new List<int>(); var blocked = new HashSet<long>(); var quota = new int[8]; for (int si = 0; si < 8; si++) { int state = si + 2; if (_count[si] >= _capacity[state]) { var removals = Enumerable.Range(0, _n) .Where(c => _state[c] == state && !_locked[c]) .OrderByDescending(c => _body[si][c] * EnvironmentScore(si, c)) .ThenBy(c => c).Take(_turnover).ToList(); foreach (int cell in removals) { ChangeState(cell, state, 0); Record(changes, cell, state, 0); blocked.Add(AssignmentKey(si, cell)); } } int room = _capacity[state] - _count[si]; quota[si] = Math.Min(_turnover, Math.Max(0, room)); } var bids = new List<Candidate>();

Appendix

Output

totalCells

Custom C# Scripts - Cellular Automata

229

for (int cell = 0; cell < _n; cell++) { if (_state[cell] != 0 || !HasActiveNeighbour(cell)) continue; for (int si = 0; si < 8; si++) { if (quota[si] == 0 || blocked.Contains(AssignmentKey(si, cell))) continue; if (IsEligible(si, cell)) bids.Add(new Candidate(_body[si][cell], cell, si)); } } var occupied = new HashSet<int>(); foreach (Candidate bid in bids.OrderBy(x => x.Score) .ThenBy(x => x.Cell).ThenBy(x => x.StateIndex)) { if (quota[bid.StateIndex] == 0 || occupied.Contains(bid.Cell)) continue; int state = bid.StateIndex + 2; ChangeState(bid.Cell, 0, state); Record(changes, bid.Cell, 0, state); quota[bid.StateIndex]--; occupied.Add(bid.Cell); } _history.Add(changes.ToArray()); _iteration++;

Appendix

// Test functional dependencies and prohibited adjacencies. private bool IsEligible(int si, int cell) { int dependencyCount = 0; bool hasSeed = false; bool forbidden = false; foreach (int neighbour in _adj8[cell]) { int state = _state[neighbour]; if (state == 1) hasSeed = true; int bit = 1 << (state + 1); if ((_dependency[si] & bit) != 0) dependencyCount++; if ((_forbidden[si] & bit) != 0) forbidden = true;

} return !forbidden && (hasSeed || dependencyCount >= 2);

230

} private bool HasActiveNeighbour(int cell) { return _adj8[cell].Any(neighbour => _state[neighbour] >= 1); } private void ChangeState(int cell, int oldState, int newState) { _state[cell] = newState; if (oldState >= 2) _count[oldState - 2]--; if (newState >= 2) _count[newState - 2]++; } private void RewindTo(int target) { while (_iteration > target)

int[] changes = _history[_iteration - 1]; for (int i = changes.Length - 3; i >= 0; i -= 3) { int cell = changes[i]; int oldState = changes[i + 1]; ChangeState(cell, _state[cell], oldState); } _history.RemoveAt(_iteration - 1); _iteration--;

} } // Calculate the eight heatmap-weighted function scores. private void BuildBodyScores( double min, double max, List<double> pr, List<double> np, List<double> ub, List<double> sun, List<double> wind, List<double> road, List<double> channel, List<double> aquifer, List<double> fault) { double flip = min + max; _body = Enumerable.Range(0, 8).Select(x => new double[_n]).ToArray(); for (int c = 0; c < _n; c++) { double iPr = flip - pr[c], iNp = flip - np[c], iUb = flip - ub[c]; double iSun = flip - sun[c], iWind = flip - wind[c]; double iRoad = flip - road[c], iChannel = flip - channel[c]; double iAquifer = flip - aquifer[c], iFault = flip - fault[c]; _body[0][c] = (pr[c] * 5 + np[c] * 10 + sun[c] * 5 + road[c] * 5 + channel[c] * 10 + iFault * 6) / 41.0; _body[1][c] = (road[c] * 5 + channel[c] * 10 + fault[c] * 10) / 25.0; _body[2][c] = (np[c] * 10 + iUb * 10 + iSun * 5 + iWind * 5 + road[c] * 8 + iChannel * 3 + aquifer[c] * 10 + fault[c] * 10) / 61.0; _body[3][c] = (np[c] * 10 + iUb * 10 + iSun * 5 + iWind * 5 + road[c] * 10 + iChannel * 10 + aquifer[c] * 10 + fault[c] * 15) / 75.0; _body[4][c] = (iPr * 5 + iNp * 5 + ub[c] * 10 + iSun * 5 + iWind * 5 + road[c] * 2 + channel[c] * 10 + iAquifer * 10 + fault[c] * 10) / 62.0; _body[5][c] = (iNp * 8 + sun[c] * 5 + iWind * 5 + channel[c] * 3 + iAquifer * 10 + fault[c] * 5) / 36.0; _body[6][c] = (pr[c] * 10 + iNp * 10 + sun[c] * 5 + iRoad * 10 + channel[c] * 5 + iAquifer * 9 + iFault * 10) / 59.0; _body[7][c] = (np[c] * 10 + iUb * 5 + road[c] * 10 + iChannel * 10 + fault[c] * 10) / 45.0; } }

// Build radius-based density neighbours with a spatial grid. private int[][] BuildDensityAdjacency(List<Point3d> points, double radius) { double inverseRadius = 1.0 / radius; var result = new int[points.Count][]; double radiusSquared = radius * radius; var grid = new Dictionary<long, List<int>>(); for (int i = 0; i < points.Count; i++) { long x = (long)Math.Floor(points[i].X * inverseRadius); long y = (long)Math.Floor(points[i].Y * inverseRadius); long z = (long)Math.Floor(points[i].Z * inverseRadius); long key = PackKey(x, y, z); if (!grid.ContainsKey(key)) grid[key] = new List<int>(); grid[key].Add(i); }

}

CultureInfo.InvariantCulture, out min) && double.TryParse(parts[1].Trim(), NumberStyles.Float, CultureInfo.InvariantCulture, out max) && min >= 0 && min < max;

for (int i = 0; i < points.Count; i++) { long x = (long)Math.Floor(points[i].X * inverseRadius); long y = (long)Math.Floor(points[i].Y * inverseRadius); long z = (long)Math.Floor(points[i].Z * inverseRadius); var neighbours = new List<int>(); for (int dx = -1; dx <= 1; dx++) for (int dy = -1; dy <= 1; dy++) for (int dz = -1; dz <= 1; dz++) { List<int> bucket; if (!grid.TryGetValue(PackKey(x + dx, y + dy, z + dz), out bucket)) continue; foreach (int j in bucket) if (i != j && points[i].DistanceToSquared(points[j]) <= radiusSquared) neighbours.Add(j); } result[i] = neighbours.ToArray(); } return result;

private static long PackKey(long x, long y, long z) { const long range = 1 << 21; long px = ((x % range) + range) % range; long py = ((y % range) + range) % range; long pz = ((z % range) + range) % range; return (px << 42) | (py << 21) | pz; } private int[][] ReadAdjacency(DataTree<int> tree, int count) { var result = Enumerable.Range(0, count).Select(x => new int[0]).ToArray(); if (tree == null) return result; for (int branch = 0; branch < tree.BranchCount; branch++) { GH_Path path = tree.Paths[branch]; int cell = path.Indices[path.Indices.Length - 1]; if (cell >= 0 && cell < count) result[cell] = tree.Branches[branch].ToArray(); } return result; } private void EmitOutput(ref object cellTree, ref object scoreTree) { var cells = new DataTree<int>(); var scores = new DataTree<double>(); for (int state = 2; state <= 9; state++) { cells.EnsurePath(new GH_Path(state)); scores.EnsurePath(new GH_Path(state)); } for (int cell = 0; cell < _n; cell++) { int state = _state[cell]; if (state < 2 || state > 9) continue; GH_Path path = new GH_Path(state); cells.Add(cell, path); scores.Add(_body[state - 2][cell], path); } cellTree = cells; scoreTree = scores; } private bool ParseDomain(string text, out double min, out double max) { min = max = 0.0; if (string.IsNullOrWhiteSpace(text)) return false; string[] parts = text.Split(new[] { “to”, “To”, “TO”, “tO” }, StringSplitOptions.None); return parts.Length == 2 && double.TryParse(parts[0].Trim(), NumberStyles.Float,

}

} private void Error(string message) { _initialized = false; Component.AddRuntimeMessage(GH_RuntimeMessageLevel.Error, message); } private static int StateColumn(int state) { if (state >= 2) return state - 2; if (state == 1) return 8; return state == 0 ? 9 : 10; } private static int Mask(params int[] states) { int result = 0; foreach (int state in states) result |= 1 << (state + 1); return result; } private static long AssignmentKey(int stateIndex, int cell) { return ((long)stateIndex << 32) | (uint)cell; } private static void Record(List<int> log, int cell, int oldState, int newState) { log.Add(cell); log.Add(oldState); log.Add(newState); }

Appendix

} // Combine immediate-neighbour compatibility and local density. private double EnvironmentScore(int si, int cell) { int[] neighbours = _adj8[cell]; double neighbourScore = 0.0; foreach (int neighbour in neighbours) neighbourScore += _W[si, StateColumn(_state[neighbour])]; if (si + 2 == 8) return neighbourScore / Math.Max(1, neighbours.Length); int density = _adjDensity[cell].Count(n => _state[n] == si + 2); double densityScore = density > _densityThreshold[si] ? DensityBonus : 0.0; return (neighbourScore + densityScore) / (neighbours.Length + 1.0); }

{

231

Agro-Productive Landscape Strategy Crop Performance Assessment

Common / local name

Z a ca t e v i ol et a / V et i v er

Species (scientific)

Ch ry sopogon z i z a n i oi des

Root structure

V ery deep, den se v ert i ca l f i brou s roots to 2 - 4 m; n on i n v a si v e cl u mp.

Root colonization / soil-binding capacity

Land-Condition Crop Matching

Time to full growth

Holds soil erosion?

V ery h i gh . R oot s t o 3 m a ct a s a F a st : f u l l h edge i n 1- 2 Y es - premi er l i v i n g n a i l wa l l ; cu ts soi l l oss 9 0 - y r. con t ou r h edge f or 9 7%. erosi on + sh a l l ow l a n dsl i des.

Grows on steep slope?

Y es - en gi n eered f or v ery st eep sl opes.

Water requirement

W i de t ol era n ce; 3 0 0 - 3 0 0 0 + mm/ y r, drou gh t & wa t erl og tol era n t.

Root-architecture schematic

Notes for Escazú use

Target land condition

T HE key t erra ce- f ormi n g h edge: pl a n t on con tou r, f orms n a tu ra l t erra ces, tra ps si l t. S teri l e cu l t i v a r, n on - i n v a si v e.

1 . S T E E P w ea t h er ed so i l ( d eg r a d ed , t h i n , er o si o n p r o n e sl o p es) Ca f e

B an an o / P latan o

M u sa x pa ra di si a ca

A ra ch i s pi n toi

F i brou s root ma ss + sh ort M odera t e. F i brou s ma t h ol ds ta proot, most l y t op 0 . 3 - 0 . 5 m. topsoi l ; best wi t h cov er crop + sh a de.

S h a l l ow f i brou s ma t f rom a corm; roots mostl y < 0 . 6 m.

M odera t e ( su rf a ce) . Den se ma t + mu l ch prot ect t opsoi l ; h ea v y pl a n t , poor deep a n ch or.

Creepi n g st ol on s root i n g a t Hi gh ( su rf a ce) . F u l l l i v i n g mu l ch n odes + sh a l l ow f i brou s root s. i n ~10 wks; su ppresses ri l l s, n ea r- tot a l su rf a ce cov er.

M edi u m: bea rs i n 3 - 4 P a rt l y - n eeds y r, f u l l 5 - 7 y r. terra ci n g + grou n d cov er to con t rol erosi on .

F a st : h a rv est i n 9 - 14 mon th s.

F a st : compl et e cov er i n 2 - 4 mon th s.

M odera t e - grown M odera t e; 12 0 0 on 2 2 0 0 mm/ y r, l i kes t erra ced/ con t ou red sh a de. sl opes.

P a rtl y - good grou n d cov er on ben ch es, n ot on st eep ra w cu t s.

G en t l e- modera t e terra ces on l y .

Y es - ex cel l en t su rf a ce erosi on cov er.

Y es - a s l i v i n g mu l ch u n der crops on sl opes.

Hi gh ; 2 0 0 0 - 2 5 0 0 mm/ y r, n eeds st ea dy moi stu re.

M odera t e; 15 0 0 3 5 0 0 mm/ y r, some drou gh t tol era n ce.

P ri ma ry h i l l si de crop of E sca z u / Cen t ra l V a l l ey ; combi n e wi t h sh a de t rees + A ra ch i s cov er on con t ou r terra ces.

G ood t erra ce/ ben ch crop a n d wa t er bu f f er; i n t erpl a n t wi th N f i x ers. A v oi d st eep u n ben ch ed sl opes.

B est i n t er- row / u n derst orey cov er f or cof f ee, orch a rds a n d terra ces; f i x es N , smoth ers weeds, prot ects soi l .

2 . F L AT w e a t h e r e d s o i l ( g en t l e b en c h es, l o w f er t i l i t y v a l l e y /f o o t sl o p e s)

3 . N E AR W AT E R / m a x w a t e r ( w et b en c h es, r i p a r i a n st r i p s, w a t er - r et en t i o n z o n es)

Ca n a de a z u c a r

Z a ca te l i mon / L emon gra ss

G u a du a a n gu sti f ol i a

S a cch a ru m of f i ci n a ru m

Cy mbopogon ci t ra t u s

Den se i n t erl ocki n g rh i z ome n et work + f i brou s root s to ~1. 5 m.

V ery h i gh . R h i z ome ma t bi n ds ba n ks a n d sl opes; cl a ssi c a n t i l a n dsl i de pl a n t.

Den se f i brou s cl u mpi n g root s, M odera t e- h i gh . T h i ck f i brou s mostl y top 1 m. cl u mps + f u l l ca n opy redu ce su rf a ce wa sh .

Den se f i brou s t u ssock root s, top 0 . 5 - 0 . 9 m.

M odera t e- h i gh . T i gh t t u ssocks work a s l ow con t ou r ba rri ers, si mi l a r to v eti v er bu t sh a l l ower.

F a st : h a rv est a bl e Y es - ex cel l en t f or cu l ms 3 - 5 y r, cov er i n sl opes, ri v erba n ks, 1- 2 y r. gu l l i es.

M edi u m: 10 - 14 mon th s per cy cl e.

F a st : den se cl u mp i n <1 y r.

Y es - st a bi l i ses st eep ba n ks a n d l a n dsl i de sca rs.

P a rt l y - good cov er G en t l e- modera t e crop on con tou r con t ou red sl opes. rows.

Y es - a s con t ou r gra ss ba rri er st ri ps.

Y es - l ow- cost con t ou r h edge on sl opes.

Hi gh ; 12 0 0 - 3 0 0 0 mm/ y r, l i kes moi st grou n d/ ri pa ri a n .

M odera t e- h i gh ; 15 0 0 - 2 5 0 0 mm/ y r.

M odera t e; 10 0 0 2 5 0 0 mm/ y r.

L ow- cost a l t ern a t i v e/ compa n i on t o v eti v er con tou r h edges; a l so a ma rketa bl e h erb/ oi l crop.

232

~1 ,2 0 0 - 3 ,0 0 0 m m /y r

Infrastructure to GROW

Infrastructure to STORE / process

Spatial requirements

Den se i n t er l o c k i n g RH I Z O M E

Co n st r u c t i o n p o l es,

Rh i z o m e /se e d l i n g

C u r i n g /t r e a t m e n t t a n k s 5 x 5 m ( ~40 0 c l u m p s/h a ) u p t o

f u r n i t u r e, l a m i n a t ed b o a r d , ( 1 2 ,0 0 0 - 3 0 ,0 0 0

n u r ser y ; p i t

( b o r a x - b o r i c a c i d b a t h ) 6 x 7 m f o r l a r g e p o l es. N eed s

m . G r i p s sh a l l o w , w ea t h er ed ,

c r a f t s. ~4,3 0 0 h a r v est a b l e

planting; light

+ c o ver ed

l o w - f er t i l i t y so i l a n d k n i t s t h e

c u l m s/h a /y r ( o r u p t o ~3 0 - E sc a z u ; p r e f e r s m o i st

w eed i n g f i r st 2 y r .

d r y i n g /st o r a g e sh e d t o m f r o m sl o p e c r e st .

m 3 /h a /y r ) . Ra i n - f e d i n ground, no irrigat ion

N o t r el l i s. M i n i m a l

p r even t f u n g a l & b o r er

o n c e est a b l i sh ed .

m a c h i n er y - h a n d

d a m a g e; si m p l e

t o o l s.

w o r k sh o p f o r v a l u e-

b u f f er f r o m st r u c t u r es; g i v e 1 - 2

Su g arcan e Sacch aru m officinarum

Deep DE N S E F I B RO US c l u m p i n g

S u g a r , p a n e l a /t a p a d u l c e ,

~1 ,5 0 0 - 2 ,5 0 0 m m /y r

La n d

O n - f a r m t r a p i c h e /m i l l

Ro w s 1 .2 - 1 .5 m a p a r t ; ~1 0 ,0 0 0 -

r o o t s ( t o p ~1 m ) . F u l l c a n o p y +

j u i c e ( g u a r o /a g u a d u l c e ) ,

( 1 5 ,0 0 0 - 2 5 ,0 0 0

p r e p /p l o u g h i n g ;

( c r u sh w i t h i n 2 4- 48 h )

1 4,0 0 0 se t t s/h a . F l a t /g e n t l y

f i b r o u s st o o l s g i v e c o n t i n u o u s

m o l a sses, f o r a g e t o p s. 6 0 -

m 3 /h a /y r ) e v e n l y

seed - c a n e set t s;

f o r p a n el a ; d r y,

g r a d ed p l o t s ea se h a r v est &

c o v e r o n f l a t w e a t h e r e d g r o u n d ; 1 2 0 t c a n e /h a /c y c l e ; st e a d y sp r e a d ; su p p l e m e n t i n

f er t i l i ser ; o c c a si o n a l v en t i l a t ed st o r e f o r

r a t o o n s r eg r o w f o r sev er a l c y c l es, l o c a l m a r k et .

m ec h a n i sa t i o n o n

d r y m o n t h s.

t o l er a t i n g l o w f er t i l i t y.

( i n t r o d u c ed )

Ban an a / Plan t ain

S h a l l o w F I B RO U S M AT f r o m a

Fru it (ban an a & cookin g

c o r m ( r o o t s < 0 .6 m ) w i t h v er y

p l a n t a i n ) - f a st , c o n t i n u o u s ( 2 0 ,0 0 0 - 2 5 ,0 0 0

~2 ,0 0 0 - 2 ,5 0 0 m m /y r

f l a t s . Ac c e s s t r a c k

d r u m s. Ca n e i t sel f n o t

f o r c a n e h a u l a g e.

st o r ed l o n g .

S u c k e r s/t i ssu e -

Ri p e n i n g /p a c k i n g

c u l t u r e p l a n t s; g o o d sh ed , w a sh i n g t a n k s,

h i g h t r a n sp i r a t i o n - a n a t u r a l

c a s h f l o w ; h a r v e s t f r o m ~9 - m 3 /h a /y r ) - o n e o f t h e DRAI N AG E ( h a t e s

w a t e r p u m p /b u f f e r . I d e a l b e si d e

1 4 m o n t h s, t h en r a t o o n s

t h i r st i est c r o p s; n eed s

st r ea m s a n d r et en t i o n p o n d s

yea r - r o u n d .

st ea d y m o i st u r e, h en c e + i r r i g a t i o n i n d r y

q u i c k - sa l e c h a i n

t h e w et n i c h e.

n eed ed n ea r m a r k et .

M u sa x p a r a d i si a c a w h er e i t a b so r b s a n d c y c l es ( i n t r o d u c ed ) m a x i m u m w a t er ; m a t + m u l c h

2 - 3 m sp a c i n g ; ~1 ,5 0 0 - 2 ,5 0 0 m a t s/h a . B e st o n f l a t - t o - g e n t l e

v en t i l a t ed c r a t es; sh o r t w et b en c h es; k eep o f f r a w

w a t er l o g g i n g r o o t s) sh el f - l i f e so c o l d o r sp el l ;

t r a n sp o r t .

su g a r /p a n e l a ; m o l a sse s

st eep c u t s ( t o p - h ea v y ) .

p r o p p i n g /st a k i n g

p r o t ec t w et t o p so i l .

a g a i n st w i n d ; h ea v y mulch.

Den se F I B RO US T US S O C K ( t o p

4 . T O P S O I L S T AB I L I S AT I O N

Lem o n g r a ss

r eq u i r ed

Cym b o p o g o n

( c o n t o u r h ed g es, t er r a c e

cit rat us

r i ser s, ex p o sed su r f a c es)

( i n t r o d u c ed )

F l a g s h i p c r o p f o r M AN AG E D S T E E P T E RRAC E S

Co f f ee

( t h e r eg i o n ' s c a sh a n c h o r

Co f f ea a r a b i c a

o n c e sl o p es a r e

( i n t r o d u c ed )

b e n c h e d /a m e n d e d )

The comparative study evaluates potential revenue crops according to root depth, soil-binding ca- Figure 8.15 Comparative Assessment of Revpacity, establishment period, erosion control, slope tolerance, water demand and economic value. enue Crops and Root-System Performance The findings demonstrate that productive vegetation can also operate as ecological infrastructure: deep or densely fibrous root systems reinforce unstable ground, while shallower crops require flatter or terraced conditions. This assessment establishes the shortlist of species suitable for the productive landscape strategy.

Water needed per year of cultivation

n et w o r k + f i b r o u s r o o t s t o ~1 .5

Bamboo sl o p e t o g e t h e r - t h e c l a ssi c a n t i - 3 6 t /h a /y r f i b e r ) f r o m y e a r G u a d u a a n g u st i f o l i a l a n d sl i d e p l a n t ; t o l er a t es p o o r 4; su st a i n a b l e a n n u a l c u t . ( n a t i ve) g r o u n d w h er e c o f f e e w o u l d f a i l .

P owerf u l bi oen gi n eeri n g pl a n t + bu i l di n g ma t eri a l ; u se on gu l l y edges, st rea mba n ks a n d sl i de repa i r.

Con t ou r- pl a n t ed c a n e gi v es den se cov er + i n come; u se wi t h v et i v er ba rri ers on t h e t erra ce edges.

Economic use / revenue

add.

uptake

B a mbu / Ca n a bra v a

Root structure & why it fits this land

Appendix

M a n i f orra j ero

Cof f ea a ra bi c a

Recommended revenue crop

~1 ,0 0 0 - 2 ,5 0 0 m m /y r

S l i p s/c l u m p

S m a l l st e a m /w a t e r

0 .6 x 0 .6 t o 0 .9 x 0 .9 m i n d en se

0 .5 - 0 .9 m ) . T i g h t c l u m p s p l a n t e d d i st i l l a t i o n + f r e sh /d r i e d

E ssen t i a l o i l ( c i t r a l ) v i a

( 1 0 ,0 0 0 - 2 5 ,0 0 0

d i v i si o n s; p l a n t

d i st i l l a t i o n st i l l +

c o n t o u r st r i p s; d o u b l es a s

o n c o n t o u r ac t as l o w l i vi n g

h er b & t ea . ~1 0 0 - 2 5 0 k g

m 3 /h a /y r ) ; f a i r l y

d en se o n c o n t o u r ;

c o n d en ser f o r o i l ;

b a r r i er so ' sp a t i a l c o st ' o v er l a p s

b a r r i er s t h a t t r a p sed i m en t a n d

o i l /h a /y r ; h a r v e st e d se v e r a l d r o u g h t t o l e r a n t , so

m i n i m a l i n p u t s; c u t d r y i n g r a c k s f o r h er b ;

h o l d t o p so i l - a r ev en u e- ea r n i n g , t i m es a y ea r .

r el i a b l e o n ex p o sed

ev er y 3 - 4 m o n t h s.

sea l ed c o n t a i n er s f o r

sh a l l o w er c o m p a n i o n t o v et i v er .

st r i p s.

o i l st o r a g e.

er o si o n c o n t r o l .

t r e l l i s/m a c h i n e r y .

F I B RO US r o o t m a ss + sh o r t

Gr een c o f f ee ( h i g h - va l u e

~1 ,2 0 0 - 2 ,2 0 0 m m /y r

N u r ser y +

W et m i l l ( b en ef i c i o ) +

~1 x 2 m ( ~5 ,0 0 0 p l a n t s/h a ) o n

t a p r o o t , m o st l y t o p 0 .3 - 0 .5 m .

sp ec i a l t y m a r k et ) . B ea r s i n

( 1 2 ,0 0 0 - 2 2 ,0 0 0

t e r r a c i n g /c o n t o u r i n d r y i n g p a t i o s / r a i se d

c o n t o u r t er r a c es; n eed s sh a d e

m 3 /h a /y r ) ; m o st l y r a i n - g ; sh a d e t r e e s

H o l d s t er r a c e t o p so i l w h en

3 - 4 y r ; ~1 - 2 t g r een

g r o w n w i t h s h a d e + Ar a c h i s

c o f f e e /h a /y r ; st r o n g e x p o r t f e d u n d e r sh a d e .

( E r y t h r i n a /I n g a ) ;

c o ver o n c o n t o u r ; t h e d ef i n i n g

va l u e.

b ed s o r m ec h a n i c a l

p r u n i n g ; p i c k er s a t

w a r eh o u se ( c o o l , l o w -

ec o n o m i c c r o p o f t h e E sc a z u /

h a r v est . Li v i n g -

h u m i d i t y).

C en t r a l Va l l ey h i l l s.

m u l c h u n d er st o r ey

Figure 8.16 Revenue Crop Selection for Specific Land Conditions in Escazú

c a n o p y + i n t er - r o w c o ver c r o p .

d r yer ; d r y p a r c h m en t

( Ar a c h i s p i n t o i ) .

The shortlisted crops are matched to specific terrain and soil conditions across the site. Bamboo is assigned to steep weathered ground, sugarcane to flatter productive plots, banana and plantain to moist areas, lemongrass to topsoil stabilisation zones, and coffee to managed steep terraces. By coordinating root performance, water requirements, cultivation infrastructure and spatial needs, the study translates crop suitability into a site-specific planting and revenue strategy. 233

Crop Allocation Suitability Heatmaps

bamboo

Heatmap

Near water bodies

banana

High slope degree

Terraces

Barriers

Top-soil control

Agriculture distribution in site

Appendix

Planting Distribution by Terrain

Suitable area distribution

coffee

lemongrass

bamboo

banana

lemon grass

coffee

sugarcane

sugar cane

The five heatmaps show the relative spatial suitability for allocating bamboo, banana, coffee, lem- Figure 8.17 Spatial Allocation Suitability ongrass and sugarcane across the site. The cultivation requirements identified in the preceding Heatmaps for the Five Selected Revenue study are translated into weighted site parameters and calculated for each location. Higher values Crops indicate areas more suitable for a particular crop, while lower values identify locations with limited compatibility. These maps provide the spatial input for assigning each species within the final agricultural distribution plan.

234

Figure 8.18 Agro-Production Zones Generated from Crop-Suitability Analysis, reproduced from Figure 5.19 Figure 8.19 Terrain-Based Distribution of the Five Selected Crop Species, reproduced from Figure 5.20

The suitability results are combined with the site topography, terrace structure and water network to produce the agricultural distribution strategy. Stabilising species such as bamboo and coffee occupy steeper terrain, banana is concentrated near wetter areas, lemongrass reinforces terrace edges and topsoil, while sugarcane is allocated to flatter productive zones. The section explains how planting changes along the slope, while the plan translates these relationships into a site-wide productive landscape.

235

Differential Growth Field Vector-Guided Functional Transformation

Definition - Differential Growth Field

Input

Vector

Directional vectors controlling the preferred growth direction of each seed

Point

Seed-cell positions generated by the cellular automata model

State

Functional state assigned to each seed

Obstacles

Functional Relationships Generate vector fields based on CA results

Vector Addition Single cell differential growth based on vector field

Channels, terrace edges or other curves that prevent propagation

Aniso

Strength of directional influence

Curves

Functional territory contours grouped into branches according to their state

Fit In Environment This makes the CA results more closely resemble the real environment.

Appendix

Output

The transformation demonstrates how the discrete cellular automata result can be converted into Figure 8.20 Functional Spatial Gradients a continuous spatial organisation. Functional relationships established by the CA model are trans- through Differential Growth, reproduced lated into directional vectors, allowing individual cells to expand, connect and reshape in response from Figure 5.24 to neighbouring programmes. The resulting territories retain the original functional logic while replacing rigid grid boundaries with smoother spatial gradients that correspond more closely to the environmental structure of the site.

236

The script is structured as a compact multi-stage computational pipeline. Instead of directly deforming the CA geometry, it first converts the site into a controlled raster field. Boundaries, channels and terrace edges are rasterised as blocked cells, small unintended gaps are automatically sealed, and seeds located outside the valid site or inside obstacles are excluded. This creates a stable computational domain while avoiding repeated and expensive geometric intersection tests during propagation. Each valid CA cell then initiates a multi-source, minimum-cost growth process managed through a priority queue. Movement aligned with the assigned vector requires less cost, while movement against it is penalised; maximum growth distance and diagonal-corner checks prevent uncontrolled expansion or crossing through barriers. Every grid location is assigned to the seed reaching it with the lowest accumulated cost. Finally, a Marching Squares procedure extracts, joins and groups the resulting boundaries by functional state. This layered structure efficiently converts the discrete CA allocation into reproducible, site-responsive functional territories while preserving the original programme relationships. 237

Custom C# Scripts - Differential Growth Field #region Usings using System; using System.Linq; using System.Collections.Generic; using Rhino.Geometry; using Grasshopper.Kernel; using Grasshopper.Kernel.Data; #endregion public class Script_Instance : GH_ScriptInstance { const int NONE = -999; static readonly int[] DX = { 1,-1,0,0,1,1,-1,-1 }; static readonly int[] DY = { 0,0,1,-1,1,-1,1,-1 }; static readonly int[,] MS = { {-1,-1,-1,-1},{3,0,-1,-1},{0,1,-1,-1},{3,1,-1,-1}, {1,2,-1,-1},{3,0,1,2},{0,2,-1,-1},{3,2,-1,-1}, {2,3,-1,-1},{0,2,-1,-1},{0,1,2,3},{1,2,-1,-1}, {3,1,-1,-1},{0,1,-1,-1},{3,0,-1,-1},{-1,-1,-1,-1} }; class Node : IComparable<Node> { public double C; public int Pixel, Seed; public long Id; public int CompareTo(Node n) { int r=C.CompareTo(n.C); return r!=0?r:Id.CompareTo(n.Id); } } long queueId;

Appendix

238

bool[] excluded=Exclude(P,walls,site,tol); double[] vx=new double[P.Count],vy=new double[P.Count]; for(int i=0;i<P.Count;i++) { double L=Math.Sqrt(V[i].X*V[i].X+V[i].Y*V[i].Y); if(L>1e-12){vx[i]=V[i].X/L;vy[i]=V[i].Y/L;} } var limits=new List<Curve>(walls); if(site!=null) limits.Add(site); double minX=P.Min(p=>p.X),minY=P.Min(p=>p.Y),maxX=P.Max(p=>p.X),maxY=P.Max(p=>p.Y); foreach(Curve c in limits) { BoundingBox b=c.GetBoundingBox(false); minX=Math.Min(minX,b.Min.X);minY=Math.Min(minY,b.Min.Y); maxX=Math.Max(maxX,b.Max.X);maxY=Math.Max(maxY,b.Max.Y); } double margin=maxGrowDist/(1-k)+h*3; minX-=margin;minY-=margin;maxX+=margin;maxY+=margin; int W=(int)Math.Ceiling((maxX-minX)/h)+2,H=(int)Math.Ceiling((maxY-minY)/h)+2; if((long)W*H>30000000L) throw new ArgumentException(“Grid too large. Increase pixelSize.”); int count=W*H; double[] cost=Enumerable.Repeat(double.MaxValue,count).ToArray(); int[] seed=Enumerable.Repeat(-1,count).ToArray();

{

}

// Initialise CA cells as propagation seeds. var queue=new SortedSet<Node>(); queueId=0; for(int i=0;i<P.Count;i++) { if(excluded[i]) continue; int x=(int)((P[i].X-minX)/h),y=(int)((P[i].Y-minY)/h); if(x<0||x>=W||y<0||y>=H) continue; int q=y*W+x; if(blocked[q]||cost[q]==0) continue; cost[q]=0;seed[q]=i;owner[q]=state[i];Push(queue,0,q,i); }

// Propagate through the vector-weighted cost field. while(queue.Count>0) { Node n=queue.Min;queue.Remove(n); if(seed[n.Pixel]!=n.Seed||n.C>cost[n.Pixel]) continue; int x=n.Pixel%W,y=n.Pixel/W; for(int d=0;d<8;d++) {

Seed);

int nx=x+DX[d],ny=y+DY[d]; if(nx<0||nx>=W||ny<0||ny>=H) continue; int q=ny*W+nx; if(blocked[q]||(d>3&&(blocked[y*W+nx]||blocked[ny*W+x]))) continue; double L=Math.Sqrt(DX[d]*DX[d]+DY[d]*DY[d]); double dot=(DX[d]*vx[n.Seed]+DY[d]*vy[n.Seed])/L; double next=n.C+h*L*(1-k*dot); if(next>maxGrowDist||next>=cost[q]) continue; cost[q]=next;seed[q]=n.Seed;owner[q]=state[n.Seed];Push(queue,next,q,n.

} } A=Contours(owner,blocked,minX,minY,h,W,H,tol);

} Curve Seal(Curve c,double gap) { if(c.IsClosed||c.PointAtStart.DistanceTo(c.PointAtEnd)>gap) return c; Curve copy=c.DuplicateCurve();if(copy.MakeClosed(gap*1.01)) return copy; var joined=new PolyCurve();joined.Append(copy);joined.Append(new LineCurve(c. PointAtEnd,c.PointAtStart));return joined; } bool[] Exclude(List<Point3d>P,List<Curve>walls,Curve site,double tol) { bool[] r=new bool[P.Count]; for(int i=0;i<P.Count;i++) { Point3d p=new Point3d(P[i].X,P[i].Y,0); r[i]=walls.Any(c=>c.IsClosed&&c.Contains(p,Plane.WorldXY,tol)!=PointContainment.Outside); if(!r[i]&&site!=null) r[i]=site.Contains(p,Plane.WorldXY,tol)==PointContainment. Outside; } return r; } void Rasterise(List<Curve> curves,bool[] blocked,double minX,double minY,double h,int W,int H) { foreach(Curve c in curves) { double[] ts=c.DivideByLength(h*.35,true)??new[]{c.Domain.Min,c.Domain.Mid,c. Domain.Max}; foreach(double t in ts)

}

Point3d p=c.PointAt(t);int x=(int)((p.X-minX)/h),y=(int)((p.Y-minY)/h); if(x>=0&&x<W&&y>=0&&y<H) blocked[y*W+x]=true;

} void Push(SortedSet<Node> q,double c,int pixel,int seed) { q.Add(new Node{C=c,Pixel=pixel,Seed=seed,Id=queueId++}); } // Convert the raster into state-based contours. DataTree<Curve> Contours(int[] owner,bool[] blocked,double minX,double minY,double h,int W,int H,double tol) { var lines=new Dictionary<int,List<Curve>>(); for(int y=0;y<H-1;y++)for(int x=0;x<W-1;x++) { int[] id={y*W+x,y*W+x+1,(y+1)*W+x+1,(y+1)*W+x}; int[] s=id.Select(i=>blocked[i]?NONE:owner[i]).ToArray(); foreach(int target in s.Where(v=>v!=NONE).Distinct()) { int mask=0;for(int i=0;i<4;i++)if(s[i]==target)mask|=1<<i; if(mask==0||mask==15)continue; if(!lines.ContainsKey(target))lines[target]=new List<Curve>(); for(int n=0;n<4;n+=2) { int a=MS[mask,n],b=MS[mask,n+1];if(a<0||b<0)break; lines[target].Add(new LineCurve(Edge(a,x,y,minX,minY,h),Edge(b,x,y,minX,minY,h))); } } } var tree=new DataTree<Curve>(); foreach(var item in lines)foreach(Curve c in Curve.JoinCurves(item.Value,Math. Max(tol,h*.05))) tree.Add(c,new GH_Path(item.Key)); return tree; } Point3d Edge(int e,int x,int y,double minX,double minY,double h) { double[,] p={{x+.5,y},{x+1,y+.5},{x+.5,y+1},{x,y+.5}}; return new Point3d(minX+p[e,0]*h,minY+p[e,1]*h,0); } } private static void RasteriseWalls( List<Curve> obstacles, Curve boundary, bool[] blocked, double minX, double minY, double gridSize, int width, int height) { var curves = new List<Curve>(obstacles); if (boundary != null) curves.Add(boundary); foreach (Curve curve in curves) { double[] parameters = curve.DivideByLength(gridSize * 0.4, true); if (parameters == null) parameters = new[] { curve.Domain.Min, curve.Domain.Mid, curve.Domain. Max }; foreach (double parameter in parameters) { Point3d point = curve.PointAt(parameter); int x = (int)((point.X - minX) / gridSize); int y = (int)((point.Y - minY) / gridSize); if (x >= 0 && x < width && y >= 0 && y < height) blocked[y * width + x] = true; } } } private void AddSeeds( List<Point3d> points, List<int> states, bool[] excluded, bool[] blocked, double[] cost, int[] seed, int[] ownerState, double minX, double minY, double gridSize, int width, int height) { for (int i = 0; i < points.Count; i++) { if (excluded[i]) continue; int x = (int)((points[i].X - minX) / gridSize); int y = (int)((points[i].Y - minY) / gridSize);

if (x < 0 || x >= width || y < 0 || y >= height) continue; int pixel = y * width + x; if (blocked[pixel] || cost[pixel] == 0.0) continue; cost[pixel] = 0.0; seed[pixel] = i; ownerState[pixel] = states[i]; HeapPush(0.0, pixel, i);

} } private void PropagateField( List<int> states, double[] vectorX, double[] vectorY, bool[] isotropic, bool[] blocked, double[] cost, int[] seed, int[] ownerState, double gridSize, double maxDistance, double anisotropy, int width, int height) { // Vector-guided expansion double currentCost = 0.0; int currentPixel = 0; int currentSeed = 0; while (HeapPop(ref currentCost, ref currentPixel, ref currentSeed)) { if (seed[currentPixel] != currentSeed || currentCost > cost[currentPixel]) continue; int x = currentPixel % width; int y = currentPixel / width; for (int direction = 0; direction < 8; direction++) { int nextX = x + DX[direction]; int nextY = y + DY[direction]; if (nextX < 0 || nextX >= width || nextY < 0 || nextY >= height) continue; int nextPixel = nextY * width + nextX; if (blocked[nextPixel]) continue; if (direction >= 4 && (blocked[y * width + nextX] || blocked[nextY * width + x])) continue; double stepLength = Math.Sqrt( DX[direction] * DX[direction] + DY[direction] * DY[direction]); double directionX = DX[direction] / stepLength; double directionY = DY[direction] / stepLength; double factor = isotropic[currentSeed] ? 1.0 : 1.0 - anisotropy * (directionX * vectorX[currentSeed] + directionY * vectorY[currentSeed]); double nextCost = currentCost + gridSize * stepLength * factor; if (nextCost > maxDistance || nextCost >= cost[nextPixel]) continue; cost[nextPixel] = nextCost; seed[nextPixel] = currentSeed; ownerState[nextPixel] = states[currentSeed]; HeapPush(nextCost, nextPixel, currentSeed); } } } private static DataTree<Curve> ExtractContours( int[] states, bool[] blocked, double minX, double minY, double gridSize, int width, int height) { // Marching-squares output var accumulators = new Dictionary<int, StateAccumulator>(); long stride = width + 2; for (int y = 0; y < height - 1; y++) for (int x = 0; x < width - 1; x++) { int[] pixels = { y * width + x, y * width + x + 1, (y + 1) * width + x + 1, (y + 1) * width + x }; int[] cornerStates = pixels .Select(pixel => blocked[pixel] ? None : states[pixel]) .ToArray(); foreach (int targetState in cornerStates.Where(s => s != None).Distinct()) { int mask = 0; for (int corner = 0; corner < 4; corner++) if (cornerStates[corner] == targetState) mask |= 1 << corner; if (mask == 0 || mask == 15) continue; StateAccumulator accumulator; if (!accumulators.TryGetValue(targetState, out accumulator)) { accumulator = new StateAccumulator(); accumulators[targetState] = accumulator; }

Appendix

private void RunScript(List<Vector3d> V, List<Point3d> P, List<int> state, List<Curve> obstacles, Curve boundary, double sealGap, double pixelSize, double maxGrowDist, double aniso, int refineFactor, ref object A) { if(P==null||V==null||state==null||P.Count==0) throw new ArgumentException(“V, P and state are required.”); if(V.Count<P.Count||state.Count<P.Count) throw new ArgumentException(“One vector and state are required per point.”); if(pixelSize<=0||maxGrowDist<=0) throw new ArgumentException(“Grid size and growth distance must be positive.”); double k=Math.Max(0,Math.Min(.95,aniso)); double h=pixelSize/Math.Max(1,Math.Min(16,refineFactor)); double gap=sealGap>0?sealGap:pixelSize*2; double tol=RhinoDocument==null?.001:RhinoDocument.ModelAbsoluteTolerance; var walls=new List<Curve>(); if(obstacles!=null) foreach(Curve c in obstacles) if(c!=null) walls.Add(Seal(c,gap)); Curve site=boundary==null?null:Seal(boundary,gap); if(site!=null&&!site.IsClosed) throw new ArgumentException(“Boundary could not be closed.”);

int[] owner=Enumerable.Repeat(NONE,count).ToArray(); bool[] blocked=new bool[count]; Rasterise(limits,blocked,minX,minY,h,W,H);

239

for (int segment = 0; segment < 2; segment++) { int edgeA = MarchingSquares[mask, segment * 2]; int edgeB = MarchingSquares[mask, segment * 2 + 1]; if (edgeA < 0 || edgeB < 0) break; long keyA = EdgeKey(edgeA, x, y, stride); long keyB = EdgeKey(edgeB, x, y, stride); if (!accumulator.Points.ContainsKey(keyA)) accumulator.Points[keyA] = EdgePoint( edgeA, targetState, cornerStates, blocked, pixels, x, y, minX, minY, gridSize); if (!accumulator.Points.ContainsKey(keyB)) accumulator.Points[keyB] = EdgePoint( edgeB, targetState, cornerStates, blocked, pixels, x, y, minX, minY, gridSize); AddLink(accumulator, keyA, keyB); AddLink(accumulator, keyB, keyA); } } } return StitchContours(accumulators); }

for (int guard = 0; guard < accumulator.Points.Count + 4; guard++) { visited.Add(current); points.Add(accumulator.Points[current]); List<long> links = accumulator.Links[current]; long next = links.FirstOrDefault(link => link != previous); if (next == 0 && !links.Contains(0)) break; if (next == start) { closed = true; break; } previous = current; current = next; } if (points.Count < 2) continue; if (closed) points.Add(points[0]); output.Add( new PolylineCurve(points), new GH_Path(state));

}

Appendix

240

private static long EdgeKey(int edge, int x, int y, long stride) { if (edge == 0) return (((long)y * stride + x) << 1); if (edge == 1) return (((long)y * stride + x + 1) << 1) | 1; if (edge == 2) return (((long)(y + 1) * stride + x) << 1); return (((long)y * stride + x) << 1) | 1; } private static void AddLink( StateAccumulator accumulator, long from, long to) { List<long> links; if (!accumulator.Links.TryGetValue(from, out links)) { links = new List<long>(2); accumulator.Links[from] = links; } if (!links.Contains(to)) links.Add(to); } private static DataTree<Curve> StitchContours( Dictionary<int, StateAccumulator> accumulators) { var output = new DataTree<Curve>(); foreach (KeyValuePair<int, StateAccumulator> entry in accumulators) { int state = entry.Key; StateAccumulator accumulator = entry.Value; var visited = new HashSet<long>(); foreach (long start in accumulator.Links.Keys) { if (visited.Contains(start)) continue; var points = new List<Point3d>(); long previous = long.MinValue; long current = start; bool closed = false;

} } return output;

private static DataTree<Curve> StitchContours( Dictionary<int, StateAccumulator> accumulators) { var output = new DataTree<Curve>(); foreach (KeyValuePair<int, StateAccumulator> entry in accumulators) { int state = entry.Key; StateAccumulator accumulator = entry.Value; var visited = new HashSet<long>(); foreach (long start in accumulator.Links.Keys) { if (visited.Contains(start)) continue; var points = new List<Point3d>(); long previous = long.MinValue; long current = start; bool closed = false; for (int guard = 0; guard < accumulator.Points.Count + 4; guard++) { visited.Add(current); points.Add(accumulator.Points[current]); List<long> links = accumulator.Links[current]; long next = links.FirstOrDefault(link => link != previous); if (next == 0 && !links.Contains(0)) break; if (next == start) { closed = true; break; } previous = current; current = next; } if (points.Count < 2) continue; if (closed) points.Add(points[0]); output.Add( new PolylineCurve(points), new GH_Path(state)); } } return output;

} private void HeapInitialise(int capacity) { // Minimum-cost queue int size = Math.Max(16, capacity); _heapCost = new double[size]; _heapPixel = new int[size]; _heapSeed = new int[size]; _heapCount = 0; }

while (true) { int left = index * 2 + 1; int right = left + 1; int smallest = index; if (left < _heapCount && _heapCost[left] < _heapCost[smallest]) smallest = left; if (right < _heapCount && _heapCost[right] < _heapCost[smallest]) smallest = right; if (smallest == index) break; HeapSwap(index, smallest); index = smallest; } return true;

}

Appendix

private static Point3d EdgePoint( int edge, int targetState, int[] cornerStates, bool[] blocked, int[] pixels, int x, int y, double minX, double minY, double gridSize) { int a = EdgeCorners[edge, 0]; int b = EdgeCorners[edge, 1]; if (cornerStates[a] != targetState) { int swap = a; a = b; b = swap; } int[] cornerX = { x, x + 1, x + 1, x }; int[] cornerY = { y, y, y + 1, y + 1 }; double interpolation = blocked[pixels[b]] ? 0.85 : 0.5; double worldX = minX + (cornerX[a] + 0.5 + (cornerX[b] - cornerX[a]) * interpolation) * gridSize; double worldY = minY + (cornerY[a] + 0.5 + (cornerY[b] - cornerY[a]) * interpolation) * gridSize; return new Point3d(worldX, worldY, 0.0); }

private void HeapPush(double cost, int pixel, int seed) { if (_heapCount == _heapCost.Length) { Array.Resize(ref _heapCost, _heapCount * 2); Array.Resize(ref _heapPixel, _heapCount * 2); Array.Resize(ref _heapSeed, _heapCount * 2); } int index = _heapCount++; _heapCost[index] = cost; _heapPixel[index] = pixel; _heapSeed[index] = seed; while (index > 0) { int parent = (index - 1) / 2; if (_heapCost[parent] <= _heapCost[index]) break; HeapSwap(parent, index); index = parent; } } private bool HeapPop(ref double cost, ref int pixel, ref int seed) { if (_heapCount == 0) return false; cost = _heapCost[0]; pixel = _heapPixel[0]; seed = _heapSeed[0]; _heapCount--; _heapCost[0] = _heapCost[_heapCount]; _heapPixel[0] = _heapPixel[_heapCount]; _heapSeed[0] = _heapSeed[_heapCount]; int index = 0;

} private void HeapSwap(int a, int b) { double cost = _heapCost[a]; _heapCost[a] = _heapCost[b]; _heapCost[b] = cost; int pixel = _heapPixel[a]; _heapPixel[a] = _heapPixel[b]; _heapPixel[b] = pixel; int seed = _heapSeed[a]; _heapSeed[a] = _heapSeed[b]; _heapSeed[b] = seed; }

241

Terrace Formation Slope-Responsive Terrace Generation

Definition - Terrace Formation

Input

Ordered points forming successive terrain contours

MinDistance

Minimum distance at which points can be paired

MaxDistance

Maximum permitted pairing distance

TerracePoints

Adjusted contour points preserving the original tree structure

Appendix

Output

ContourPoints

Equal contour

Terrace pattern

The study demonstrates how regular terrain contours are transformed into a differentiated terrace system responding to local slope conditions. Successive contour lines are selectively connected and adjusted, producing wider terrace bands across gentler terrain and a denser sequence across steeper areas. The resulting formation retains the overall topographic structure while converting the continuous slope into a series of spatial platforms suitable for cultivation, circulation and subsequent programme integration.

242

Terrace formation

Figure 8.21 Parametric Terrace Formation on the Selected Site: Terrain Model and Slope-Responsive Contour Transformation, compiled from Figures 5.21 and 5.22

The script is designed around the hierarchical structure of Grasshopper DataTrees, treating each branch as a successive terrain contour. A cumulative branch-index system temporarily converts the tree into global point references, allowing points on different contours to be compared without losing their original branch paths. Rather than testing every point against the complete terrain dataset, each point searches only the immediately adjacent contours, significantly reducing unnecessary calculations and ensuring that connections remain locally responsive to the terrain sequence. Minimum and maximum distance parameters further restrict pairing to a controlled spatial range. A mutual-nearest-neighbour rule provides the key geometric control: two points are connected only when each identifies the other as its closest valid candidate. This prevents multiple contour points from collapsing onto the same location and produces stable one-to-one relationships between successive contours. Accepted pairs are relocated to a shared midpoint, while unmatched points remain unchanged. A binary-search helper efficiently converts global indices back into their original branches, enabling the final point tree to retain the complete Grasshopper hierarchy. This carefully structured process creates a deterministic and adjustable terrace system in which pairing distances directly control terrace width, frequency and responsiveness to local slope variation. 243

Custom C# Scripts - Terrace Formation #region Usings using System; using System.Collections.Generic; using Rhino.Geometry; using Grasshopper.Kernel; using Grasshopper.Kernel.Data; #endregion

{

} } bool withinPairingRange = nearestDistance > MinDistance && nearestDistance <= MaxDistance; candidates[globalIndex] = nearestIndex >= 0 && withinPairingRange ? nearestIndex : -1;

} } DataTree<Point3d> result = new DataTree<Point3d>();

// Create global point indices int totalPoints = 0; for (int branch = 0; branch < branchCount; branch++) { branchStart[branch] = totalPoints; totalPoints += ContourPoints.Branches[branch].Count; } int[] candidates = new int[totalPoints];

244

// Find the nearest point on adjacent contours for (int branch = 0; branch < branchCount; branch++) { List<Point3d> points = ContourPoints.Branches[branch]; for (int index = 0; index < points.Count; index++) { int globalIndex = branchStart[branch] + index; Point3d point = points[index]; double nearestDistance = double.MaxValue; int nearestIndex = -1; for (int offset = -1; offset <= 1; offset += 2) { int adjacentBranch = branch + offset; if (adjacentBranch < 0 || adjacentBranch >= branchCount) continue; List<Point3d> adjacentPoints = ContourPoints.Branches[adjacentBranch]; for (int adjacentIndex = 0; adjacentIndex < adjacentPoints.Count; adjacentIndex++)

}

return pointTree.Branches[lower][localIndex];

Appendix

public class Script_Instance : GH_ScriptInstance { private void RunScript( DataTree<Point3d> ContourPoints, double MinDistance, double MaxDistance, ref object TerracePoints) { if (ContourPoints == null || ContourPoints.BranchCount == 0) { TerracePoints = new DataTree<Point3d>(); return; } int branchCount = ContourPoints.BranchCount; int[] branchStart = new int[branchCount];

private Point3d GetPoint( DataTree<Point3d> pointTree, int[] branchStart, int globalIndex) { int lower = 0; int upper = branchStart.Length - 1; while (lower < upper) { int middle = (lower + upper + 1) / 2; if (branchStart[middle] <= globalIndex) lower = middle; else upper = middle - 1; } int localIndex = globalIndex - branchStart[lower];

double distance = point.DistanceTo( adjacentPoints[adjacentIndex] ); if (distance < nearestDistance) { nearestDistance = distance; nearestIndex = branchStart[adjacentBranch] + adjacentIndex; }

// Merge mutual pairs at their midpoint for (int branch = 0; branch < branchCount; branch++) { GH_Path path = ContourPoints.Paths[branch]; List<Point3d> points = ContourPoints.Branches[branch]; for (int index = 0; index < points.Count; index++) { int globalIndex = branchStart[branch] + index; int pairedIndex = candidates[globalIndex]; bool isMutualPair = pairedIndex >= 0 && candidates[pairedIndex] == globalIndex; if (isMutualPair) { Point3d pairedPoint = GetPoint( ContourPoints, branchStart, pairedIndex ); Point3d midpoint = new Point3d( (points[index].X + pairedPoint.X) * 0.5, (points[index].Y + pairedPoint.Y) * 0.5, (points[index].Z + pairedPoint.Z) * 0.5 ); result.Add(midpoint, path); } else { result.Add(points[index], path); } } } TerracePoints = result;

245

Functional Clustering Functional Cluster Transitions

Definition - Functional Clustering

Input

Program zones

Cluster formation

Output

Points

Centre point of each program cell

Vectors

Direction controlling cluster propagation

States

Program state assigned to each cell

ReachFactor

Converts boundary depth into propagation distance

SizeFactor

Converts boundary depth into cluster capacity

ClusterId

Segments generated during each iteration

ClusterSizeList

Appendix

Porosity and size variation

Smoothen clusters

Appendix

Number of cells within each cluster

A layered computational architecture separates spatial indexing, directional evaluation, capacity-controlled merging and geometric reconstruction. Cell spacing is detected automatically and each point is converted into an integer grid coordinate, allowing neighbouring cells to be retrieved efficiently through a packed-coordinate dictionary. Distance from the site boundary is then calculated for every cell and translated into two local controls: propagation reach increases with boundary depth, while cluster capacity grows proportionally to its square. This enables larger clusters to form within deeper regions while maintaining smaller, more fragmented groups near exposed edges. The sequence demonstrates how the dispersed cells produced by the earlier allocation process Figure 8.22 Functional Cluster Transitions are consolidated into coherent functional clusters. Directional relationships connect nearby cells across the Generated Landscape, reproduced of the same programme, while variations in available space generate clusters of different sizes from Figure 6.3 and densities. The final smoothing stage removes the rigid cellular appearance and produces continuous functional territories, retaining local programme diversity while establishing spatial units suitable for subsequent architectural and landscape development.

246

Potential connections are explored through a bounded grid search and retained only when cells share the same functional state and satisfy the permitted directional angle. Each connection receives a score based on vector alignment and propagation distance, so strongly aligned and nearby relationships are evaluated first. A capacity-aware union-find structure then merges compatible groups efficiently while preventing their combined size from exceeding locally derived limits. Finally, the clustered cells are reconstructed as joined outlines, organised by programme state and softened through controlled geometric smoothing. This modular design allows reach, directionality, capacity and boundary response to be adjusted independently, producing varied but reproducible functional territories from the same initial allocation.

247

Custom C# Scripts - Functional Clustering #region Usings using System; using System.Linq; using System.Collections.Generic; using System.Drawing; using Rhino.Geometry; using Grasshopper.Kernel; using Grasshopper.Kernel.Data; #endregion

Appendix

{

248

if (Points == null || Points.Count == 0 || Vectors == null || Vectors.Count != Points.Count || States == null || States.Count != Points.Count) { Print(“Invalid point, vector or state data.”); return; } int count = Points.Count; ReachFactor = ReachFactor > 0 ? ReachFactor : 1.0; MaxReach = MaxReach > 0 ? MaxReach : 3; AngleTol = AngleTol > 0 ? AngleTol : 75.0; EpsMagnitude = EpsMagnitude > 0 ? EpsMagnitude : 0.1; CapR = CapR > 0 ? CapR : 1; SizeFactor = SizeFactor > 0 ? SizeFactor : 2.0; MinClusterSize = Math.Max(1, MinClusterSize); MaxClusterSize = Math.Max(MinClusterSize, MaxClusterSize); SmoothSteps = Math.Max(0, SmoothSteps); double spacing = GetSpacing(Points); double inverseSpacing = 1.0 / spacing; double cosTolerance = Math.Cos(AngleTol * Math.PI / 180.0); var grid = new Dictionary<long, int>(); int[] gridX = new int[count]; int[] gridY = new int[count]; for (int i = 0; i < count; i++) { gridX[i] = (int)Math.Round(Points[i].X * inverseSpacing); gridY[i] = (int)Math.Round(Points[i].Y * inverseSpacing); grid[Pack(gridX[i], gridY[i])] = i; } double[] depth = CalculateDepth( Points, States, Boundary, grid, gridX, gridY, spacing); int[] reach = new int[count]; int[] capacity = new int[count];

List<Edge> edges = BuildEdges( Points, States, grid, gridX, gridY, reach, direction, hasDirection, cosTolerance); edges.Sort((x, y) => y.Score.CompareTo(x.Score)); int[] parent = Enumerable.Range(0, count).ToArray(); int[] clusterSize = Enumerable.Repeat(1, count).ToArray(); int[] minimumCapacity = capacity.ToArray(); Func<int, int> find = null; find = index => { while (parent[index] != index) { parent[index] = parent[parent[index]]; index = parent[index]; } return index; }; foreach (Edge edge in edges) { int rootA = find(edge.A); int rootB = find(edge.B); if (rootA == rootB) continue; int mergedSize = clusterSize[rootA] + clusterSize[rootB]; int mergedCapacity = Math.Min( minimumCapacity[rootA], minimumCapacity[rootB]); if (mergedSize > mergedCapacity) continue; parent[rootA] = rootB; clusterSize[rootB] = mergedSize; minimumCapacity[rootB] = mergedCapacity; } int[] ids = new int[count]; var remap = new Dictionary<int, int>(); for (int i = 0; i < count; i++) { int root = find(i); if (!remap.ContainsKey(root)) remap[root] = remap.Count; ids[i] = remap[root]; } int numberOfClusters = remap.Count; var members = new List<int>[numberOfClusters]; for (int c = 0; c < numberOfClusters; c++) members[c] = new List<int>(); for (int i = 0; i < count; i++) members[ids[i]].Add(i); DataTree<Point3d> pointTree = new DataTree<Point3d>(); DataTree<Polyline> outlineTree = new DataTree<Polyline>(); DataTree<Polyline> stateTree = new DataTree<Polyline>(); Point3d[] centers = new Point3d[numberOfClusters]; int[] sizes = new int[numberOfClusters]; Color[] colors = new Color[count]; for (int state = -1; state <= 9; state++) stateTree.EnsurePath(new GH_Path(state));

for (int c = 0; c < numberOfClusters; c++) { GH_Path path = new GH_Path(c); Vector3d sum = Vector3d.Zero; foreach (int index in members[c]) { pointTree.Add(Points[index], path); sum += (Vector3d)Points[index]; colors[index] = Color.FromArgb( 90 + (c * 67) % 150, 90 + (c * 97) % 150, 90 + (c * 131) % 150); } centers[c] = new Point3d(sum / members[c].Count); sizes[c] = members[c].Count; List<Polyline> outlines = BuildOutlines( members[c], gridX, gridY, spacing, Points[0].Z, SmoothSteps); GH_Path statePath = new GH_Path(States[members[c][0]]); foreach (Polyline outline in outlines) { outlineTree.Add(outline, path); stateTree.Add(outline, statePath); } } ClusterId = ids.ToList(); ClusterPoints = pointTree; PointColors = colors.ToList(); ClusterOutline = outlineTree; StateOutline = stateTree; ClusterCenter = centers.ToList(); ClusterSizeList = sizes.ToList();

} private double[] CalculateDepth( List<Point3d> points, List<int> states, Curve boundary, Dictionary<long, int> grid, int[] gx, int[] gy, double spacing) { int count = points.Count; double[] depth = new double[count]; int maxRing = (int)Math.Ceiling(new BoundingBox(points).Diagonal.Length / spacing) + 2; for (int i = 0; i < count; i++) { double nearest = double.MaxValue; for (int ring = 0; ring <= maxRing; ring++) { if (nearest <= (ring - 1) * spacing) break; for (int dx = -ring; dx <= ring; dx++) for (int dy = -ring; dy <= ring; dy++) { if (Math.Max(Math.Abs(dx), Math.Abs(dy)) != ring) continue; int neighbour; if (!grid.TryGetValue(Pack(gx[i] + dx, gy[i] + dy), out neighbour)) continue; if (states[neighbour] == states[i]) continue; nearest = Math.Min(nearest, points[i].DistanceTo(points[neighbour])); } } if (boundary != null && boundary.IsValid) { double parameter; if (boundary.ClosestPoint(points[i], out parameter)) nearest = Math.Min(nearest, points[i].DistanceTo(boundary.PointAt(parameter))); } depth[i] = nearest == double.MaxValue ? maxRing : nearest / spacing; } return depth; } private List<Edge> BuildEdges( List<Point3d> points, List<int> states, Dictionary<long, int> grid,

{

int[] gx, int[] gy, int[] reach, Vector3d[] direction, bool[] hasDirection, double cosTolerance) int[] dx = { 1, -1, 0, 0, 1, 1, -1, -1 }; int[] dy = { 0, 0, 1, -1, 1, -1, 1, -1 }; var edges = new List<Edge>(); for (int source = 0; source < points.Count; source++) {

Appendix

public class Script_Instance : GH_ScriptInstance { private struct Edge { public double Score; public int A, B; public Edge(double score, int a, int b) { Score = score; A = a; B = b; } } private void RunScript( List<Point3d> Points, List<Vector3d> Vectors, Curve Boundary, List<int> States, double ReachFactor, int MaxReach, double AngleTol, double EpsMagnitude, int CapR, double SizeFactor, int MinClusterSize, int MaxClusterSize, int SmoothSteps, ref object ClusterId, ref object ClusterPoints, ref object PointColors, ref object ClusterOutline, ref object StateOutline, ref object ClusterCenter, ref object ClusterSizeList)

Vector3d[] direction = new Vector3d[count]; bool[] hasDirection = new bool[count]; for (int i = 0; i < count; i++) { Vector3d vector = new Vector3d(Vectors[i].X, Vectors[i].Y, 0); hasDirection[i] = vector.Length >= EpsMagnitude; if (hasDirection[i]) vector.Unitize(); direction[i] = vector; int r = Clamp((int)Math.Round(depth[i] * ReachFactor), 1, MaxReach); reach[i] = hasDirection[i] ? r : Math.Min(r, CapR); capacity[i] = Clamp( (int)Math.Round(SizeFactor * depth[i] * depth[i]), MinClusterSize, MaxClusterSize); }

private double GetSpacing(List<Point3d> points) { double spacing = double.MaxValue; for (int i = 1; i < points.Count; i++) { double distance = points[0].DistanceTo(points[i]); if (distance > 1e-9) spacing = Math.Min(spacing, distance); } return spacing; }

private static int Clamp(int value, int minimum, int maximum) { return Math.Max(minimum, Math.Min(maximum, value)); } private static long Pack(int x, int y) { return ((long)(x + 1000000) << 32) | (uint)(y + 1000000); } private List<Polyline> BuildOutlines( List<int> members, int[] gx, int[] gy, double spacing, double z, int smoothSteps) { var curves = new List<Curve>(); foreach (int i in members) { var rectangle = new Polyline(new[] { new Point3d((gx[i] - 0.5) * spacing, (gy[i] - 0.5) * spacing, z), new Point3d((gx[i] + 0.5) * spacing, (gy[i] - 0.5) * spacing, z), new Point3d((gx[i] + 0.5) * spacing, (gy[i] + 0.5) * spacing, z), new Point3d((gx[i] - 0.5) * spacing, (gy[i] + 0.5) * spacing, z), new Point3d((gx[i] - 0.5) * spacing, (gy[i] - 0.5) * spacing, z) }); curves.Add(rectangle.ToNurbsCurve());

249

}

250

} private Polyline SmoothLoop(Polyline loop, int steps) { var points = loop.ToList(); if (points.Count > 1 && points[0].DistanceTo(points.Last()) < 1e-9) points.RemoveAt(points.Count - 1); for (int step = 0; step < steps; step++) { var next = new List<Point3d>(); for (int i = 0; i < points.Count; i++) { Point3d a = points[i]; Point3d b = points[(i + 1) % points.Count]; next.Add(a * 0.75 + b * 0.25); next.Add(a * 0.25 + b * 0.75); } points = next; } points.Add(points[0]); return new Polyline(points); }

Appendix

} Curve[] unions = Curve.CreateBooleanUnion(curves, spacing * 0.001); var result = new List<Polyline>(); if (unions == null) return result; foreach (Curve curve in unions) { Polyline polyline; if (curve.TryGetPolyline(out polyline)) result.Add(SmoothLoop(polyline, smoothSteps)); } return result;

251

Design Proposal

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254

Design Proposal

Decoding Riparians (MSc)