The Sustainable Housing Intelligent Tool

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The Sustainable Housing Intelligent Tool Computational Urban Design II - MaCT01 Alvaro Cerezo Inigo Esteban Leyla Saadi Tugdual Sarazin


Table of Contents: 1. Abstract 2. Introduction 3. Site Opportunities 4. Related Work 5. Methodology 6. Procedural Geometry/Data Generation 7. Demonstration 8. Discussion and Next Steps 9. Conclusion 10. References



The Sustainable Housing Intelligent Tool

Abstract: The Sustainable Housing Intelligent Tool aims to help urban designers and decision makers to develop walkable, energy efficient and social neighborhoods. This is achieved through a series of iterative computational generation which explores the best land-use and building geometry solutions for optimal environmental performance and livability.

THE SITE

Poblenou, District @22

Introduction: The selected site for this study is district 22@ in Poblenou, Barcelona. The area is characterized by low urban density due to the fact that the land use was historically industrial and has recently been going through an urban reform process aiming at regenerating and activating the entire district with residential, creative and business activities.

A Process of social participation has taken place in order to identify the best intervention in the area. The main takeaways were that the 22@ urban model with its typology of buildings (offices and hotels) does not allow a cohesive neighborhoods development due to lack of residential density and prevents the development of everyday life. The impact of the heights and architectural contrast of the new 22@ buildings (DiagonalGlorias and Diagonal-Selva de Mar) with their surroundings does not favor the identity and cohesion of public and human friendly spaces. Site Opportunities: District 22@ is a dynamic area of the city based on it´s urban centrality and good level of connectivity to the city. It also enjoys a strategic position in the technological sector and has seen a great growth and increased density of business relocating into the area. And finally, the district has a large area of vacant land which is a rarity in a city as dense as Barcelona. This provides a great opportunity for development and innovation in a well-balanced residential and commercial district.

CURRENT SITUATION Poblenou, District @22

Create a new urban model based on well-livability and energy efficiency Learnings from social participation: ●

THE SITE

Poblenou, District @22

Density Diversity Sustainability

The 22@ urban model with its typology of buildings (offices and hotels) does not cohere the neighbourhoods and prevents the development of everyday life. The impact of the heights and architectural contrast of the new 22@ buildings (Diagonal-Glorias and Diagonal-Selva de Mar) with their surroundings does not favour the identity and cohesion of the spaces.

Opportunities: ● ● ●

Dynamic area of the city based on it´s urban centrality Strategic position on the technological sector Infrastructure communications

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Related Work:

Land use plan Gran de Sant Andreu The renovation of the urbanization of the main street of Sant Andreu, a civic and structuring centre of the neighborhood, will substantially transform its current configuration and surroundings in the immediate future. The main objective of this renovation is to pacify the street and renew all the elements of its urbanization (paving, services, vegetation, etc.), Enhancing the urban quality of this road. In this context, the municipal administration considers that the renovation must be accompanied by a regulation of the uses that reinforces the commercial axis and controls ROJECT the REFERENCES implementation of activities.

Ecological City Simulator Block’hood This ecological city simulator, Block’hood, which allows players to build their own archeology-style structures for humans and other species to coexist, all while managing a range of environmental and engineering conditions. PROJECT REFERENCES Ecological City Simulator Block'hood

This ecological city simulator, Block'hood, which allows players to build their own arcology-style structures for humans and other species to coexist, all while managing a range of environmental and engineering conditions.

use plan Gran de Sant Andreu

main uring ntially and main y the f its ation, oad.

ation t be that ntrols

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The Sustainable Housing Intelligent Tool

Density: Compact and connected urban environment:

NEW URBAN MODEL dense-diverse-sustainable

Site scale

• Strategy to reduce our carbon footprints and reverse the effects of climate change. • Denser cities encourage walking, allow for shorter trip lengths and complement the benefits of mixed land use.

Block scale

Design Intentions

Maximize green public space: Create a balance between private and public space: Mixity

Connectivity

Green Public Space

Energy efficiency

Density

Methodology: A new urban model: dense, diverse and sustainable Through this tool, we aim to achieve five main objectives: mixity, connectivity, energy efficiency, density and maximize green public space. Mixity: Create and manage land use diversity: • Encourage non-automobile based modes of travel such as walking and cycling. • Establishing the number of potential destinations in a neighborhood. Connectivity: Improve the user walkability experience: • Create a more accessible urban environment (urban structure + center to center block). • Improve the desirable paths within the neighborhood. Energy efficiency: Drive the design through solar occlusion parameter to:

• Public spaces as anchors for social interactions. • Green areas to improve the quality of the neighborhood and increase the contact with nature. Intervention phasing: The phasing strategy was developed based on existing land uses in the site. These were collected from Barcelona open data platform and analyses in order to identify the best sequence of intervention. The site includes a number of vacant lands, these plots will be used as the first phase of generation. New land-uses and buildings will be generated in these plots first. The second phase of intervention will be to clear out some old industrial lands, and re-purpose these plots for a better mix of uses in order to achieve the project objective set previously.

INTERVENTION PHASING

Renovation of the industrial areas

• Reduce energy consumption for heating and lighting. • Improve wellbeing through exposure to natural light. Current site

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Phasing Strategy Maps INTERVENTION PHASING

Renovation of the industrial areas

Procedural Geometry/Data Generation: The simulation is split into two parts, the land use generation and the block aggregation.

URBAN MODEL SIMULATIONS

Fixed Plots no intervention

INTERVENTION PHASING

Renovation of the industrial areas

Phase 01 Vacant Plots

INTERVENTION PHASING

Renovation of the industrial areas

The building use generation is done through an agent based algorithm, the tool is able to generate the optimal land-use based on preset walking distance to all required services in a residential neighborhood. Creating a more diverse urban model that improves accessibility and connectivity conditions. LAND USE SIMULATION

Phase 02 Purly Industrial

Land-use optimization algorithm: Concept This algorithm is inspired by Louis Sullivan’s axiom “Form follows function”. So the main idea is to represent the environment only by functions and to convert them into forms at 7


The Sustainable Housing Intelligent Tool

the end of the process. Therefore all places crossed by a “city user” (streets, buildings, parks, ...) are functions and the algorithm follows these rules : • Functions have positive or negative impacts on users. • User path is modeled as a sequence of functions. • A city (or a studied district) is represented as a network of functions and users choose the path that minimizes the negative effects. Note: In our implementation we only use streets length to create a negative impact. So the algorithm will choose the shortest path for each user. But all kinds of effects could be modelized (e.g. number of trees, number of shops, solar exposition, …). From this representation the algorithm computes a score for every user’s path and sums them. By testing different locations it also updates users’ paths and consequently, the final score. This process is integrated into an optimization function to find the best land-use based on users paths scores.

METHODOLOGY

Graph Algorithm for land use distribution

METHODOLOGY

Graph Algorithm for land use distribution

Comparison with other approaches: Because this approach is user centric, by simulating users experience it differs greatly from an index approach that counts the number of services and amenities in an area. For example, let’s consider a user who likes to cross parks. In our approach if the user has a high number of parks around his/her residence but if they are not on his path during their daily trips it will not have a positive impact on the simulation score. Contrary to an index approach which will count the number of parks and increase the score for each of them. Implementation: To implement this algorithm the process is divided into 3 components. A graph network to modelize the city, a multi-agent system to simulate users and an optimization algorithm. LAND USE GENERATION

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Graph network: The graph network is a data structure to represent the connections of the city. It is produced from the roads and buildings of the city (or studied district). A graph structure is made up of nodes and edges. In this algorithm each building and crossing are represented as nodes. They are linked by edges that represent streets and connections with buildings. Each edge contains the parameters of the function that interacts with the user. This graph algorithm was based on the Python library NetworkX. Multi-agent system: Simulated users are represented as agents. Agents are defined by: • Their dwelling which is a node on the graph. • The type of amenities they wants to reach. All the paths between their dwelling and the amenities are evaluated using the Dijkstra algorithm. This algorithm computes the best path between the dwelling node and the amenity building node. The best path is defined by the path that minimizes the negative effects. The end of this part computes the final connectivity score of the current landuse which is the sum of each user’s best path score. Note: Because we only use the length as a parameter, the best path is the shortest one.

Integration: The land-use function required the road network and the agents as inputs and returned the optimal land-uses. It is integrated into Grasshopper with a client-server architecture. The Python land-use program is encapsulated into a Flask web server and it is called by Grasshopper client with a hops component. This software architecture is particularly interesting because the land-use service can then be simply deported to a remote server. This remote service can be provided easily to any Grasshopper program or even to a web application. It then becomes a multi-client service.

Optimization: This part creates multiple land-uses and finds the one that minimizes the connectivity score. The optimization of the type of land-use is achieved by the library Hyperopt. This library is based on the Tree of Parzen Estimators algorithm.

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The Sustainable Housing Intelligent Tool

Block aggregation algorithm: Once the optimal land-use for the site is obtained from the previous algorithm, each user can select a particular block in the area to start a new generation process in the building scale. The tool aims to generate alternative building typologies to those found in the Eixample while maintaining the building density but seeking to improve energy efficiency, increasing solar exposure and freeing up part of the ground floor.

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The core of the tool relies on a discrete aggregation procedure, allowing generation of a new specific urban fabric from the combination of different modules. Inputs: Firstly, two inputs need to be defined by the user to start running the tool: the final building coverage ratio to achieve on each selected lot and the type of modules to be used in the aggregation process.

Footprint: allows the user to determine the desired proportion of private and public space on the ground floor of each lot. The way to set this proportion is through the building coverage ratio, which compares the size of the next building floor plate with the total size of the plot to develop. Type of Modules: a catalogue of modules are previously designed for each land use category, based on their shapes, areas and future occupancy. The user can select which types of modules should be used in the aggregation process, based on the planned land use of the lot and particular building requirements. Methodology: Once the maximum ground coverage area and types of modules are defined by the user, the model is able to iteratively add these modules by checking in the loop the solar impact on each of them and deciding at each step where to place the next unit to maximize the sun exposure. Every unit inside each module includes basic information necessary for the aggregation process, such as the final geometry and a set of possible connections location and orientation. This set of connectors define the topological graph of the part, which is then used to define the possibilities of aggregation with other parts. The rule to select the best option to orient one module over a selected connection of another one is based on the measurement of the shadow cast by each option over the rest of the modules already aggregated in the model. The selected option will be the one that casts the least shadow and receives the most solar radiation at the same time.

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METHODOLOGY

Discrete Aggregation based on sun occlusion

Outputs: Every building result is measured by three different metrics, that allow the user to know how close the result is to the initial objectives of keeping a similar building density (Floor Area Ratio) of an Eixample typical block, but with a bigger public open space on the ground floor and a better overall sun exposure. Density and Coverage metrics are extracted through the comparison of the sum of the built-up area of all the modules used with the original lot area. On the other hand, the sun radiation is measured by a Ladybug simulation to obtain the total number of kW/h received by each solution. BLOCK SIMULATION

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The Sustainable Housing Intelligent Tool

The User Interface The user interface is composed of three main steps of user interaction. When interesting the tool, the space identifies the site location and allows the user to view the current morphology and uses on the site. Once the vacant plots or desired plots are selected, the first part of the simulation will run. This simulation will generate the optimal land use for the selected plots. Each option will show the different percentages of land uses provided, the connectivity score based on the

shortest walk to all desired uses and finally the area of the open space provided in this option. Finally the algorithm will choose the best option which achieves the highest connectivity score. The next step will be to identify the city block to run a building aggregation. Once this is selected the user will be provided with a number of modules to be used for the architectural aggregation of the buildings. For this pilot tool, we have developed 3 main uses. The uses provided are residential, offices and

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services. Within the residential use, the tool provides a selection between five modules. The modules are based on four different apartment sizes and one shared community space. For the offices, three different sizes of office units are provided. And finally, two modules are provided for general services. The user can select the most suitable modules based on their commercial goals and/or client brief. Once the desired modules are selected for each land use, the tool will start generating

building massing options from the modules. This is a discrete aggregation which tries to maximize sun exposure while achieving a set height and number of modules for each building. The user can test different inputs and modules until the preferred option is achieved. For every aggregation the tool will measure a number of metrics to allow the user to compare all options. The metrics measured are solar radiance, population density and open public space.

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The Sustainable Housing Intelligent Tool

Discussion and Next Steps:

References:

Next step should be to try to apply our discrete aggregation on multiple lots at the same time, considering that the growing process of each building will be affected by the growing of the ones around it, as well as by the existing urban fabric. The code would be the same as the single discrete aggregation but in this case we will have as input multiple starting points for the growing process and a particular identifier for each lot that allow us to identify the modules to be used on each process, based on the land used previously assigned by our script. This multiple growing process will be more organic and will allow us to merge the generation of the whole defined site in one single step, unify all required inputs on a unique interface, as it happens with our land use generator. The main issue we are facing once we up scale our block generator to the whole site is that both processing time will increase exponentially, since the selection process will need to analyze all the possible locations in every lot each single step inside our loop. The decision to apply our discrete aggregation in a single block or the whole site would be based on the users time and scale requirements and their available computing power.

Morphocode Map Explorer. https://explorer. morphocode.com/map

Conclusion:

Cadastral Urban Data. https://www. sedecatastro.gob.es/Accesos/ SECAccDescargaDatos.aspx Open data Barcelona. https://opendataajuntament.barcelona.cat/data/ca/dataset/ taula-map-illa Martí Garcia, Cristian. “TFM. La Radiación Solar en el Ensanche de Barcelona”. 2014. PDF File https://upcommons.upc.edu/ handle/2117/85980 Gausa, Manuel. “Barcelona “multiciudad”: hacia una nueva evolución urbana” Metrópolis, La razón en la ciudad: el Plan Cerdà, Otoño 2009. http://www.anycerda.org/congres/ documentacio/postmetropolis/docs/A/A21.pdf Fundació Barcelona Institute of Technology for the Habitat. Ajuntament de Barcelona. “Pacte. Cap a un Poblenou amb un 22@ més inclusiu i sostenible”. 2019. PDF File https://ajuntament.barcelona.cat/ barcelonallibres/ca/publicacions/pacte-cap-unpoblenou-amb-un-22-mes-inclusiu-i-sostenible

With the help of the discrete aggregation and the agent algorithm, we are able to explore hundreds of possibilities which would not otherwise be possible manually. While the model still requires development and aesthetic enhancement, it offers a great tool for designers to achieve optimal, environment and user sensitive models while achieving commercial requirements often set by clients or stakeholders. 14


The Sustainable Housing Intelligent Tool is a project of IAAC, Institute for Advanced Architecture of Catalonia developed at Master in City & Technology in 2020/21 by students: Alvaro Cerezo, Inigo Esteban, Leyla Saadi, Tugdual Sarazin and faculty: Eugenio Bettuchi, Iacopo Neri and Alex Mademochoritis.


The Sustainable Housing Intelligent Tool - 2021