Rumoer 82: Data

Building Technologist Technology periodical forA(BouT) the Building featuring OMRT, Eren Gozde Anil, Namrata Baruah, ETH Zurich, Saint Gobain, Superworld, CORE Studio, Link Arkitektur, Packhunt, Vasilka Espinosa, AiDapt Lab,Bout

82. Data

Cover page Distribution of access graphs of small-scale residential floor plans.

A floor plan can be represented in various way, e.g., as an image. A more unknown, but noticeable, representation is the access graph: a topologicallystructured object in which nodes are rooms and edges are room connections, i.e., a door or passage.

An access graph has the potential to directly inform us about properties or functions of a building or part of it. For example, access graphs provide information about the closeness of rooms which might be needed for determining privacy, i.e., shortest distance between two nodes in the graph, or whether a room is central and suited to be a communal area i.e., a node with many edges. Large scale analyses of floor plan access, remarkably, remain a relatively unexplored territory. However, the analysis of access graphs has the potential to reveal common and uncommon compositional patterns which could indirectly help the design of and research about buildings. The picture depicts the distribution of access graphs of 3000 randomly picked real-world floor plans of residential building in Asia from the RPLAN dataset.

The number above the graph as well as the shade of blue indicates the graph's occurrence. Note that the position of a node is not necessarily the centroid of the corresponding room: the access graphs solely depict the topological relations between the rooms.

Do you see any remarkable patterns? @AiDAPT https://www.tudelft.nl/ai/aidapt#c871580

RUMOER 82 - DATA 4th Quarter 2023 28th year of publication Rumoer Rumoer is the primary publication of the student and practice association for Building Technology ‘Praktijkvereniging BouT’ at the TU Delft Faculty of Architecture and the Built Environment.

BouT is an organisation run by students and focused on bringing students in contact with the latest developments in the field of Building Technology and with related companies.

Every edition is covering one topic related to Building technology.

Different perspectives are shown while focussing on academic and graduation topics, companies, projects and interviews.

With the topic 'Data', we are publishing our 82nd edition.

Printing www.printerpro.nl Circulation The RUMOER appears 3 times a year, with more than 150 printed copies and digital copies made available to members through online distribution.

Membership Amounts per academic year (subject to change): € 10,- Students € 30,- PhD StudWents and alumni € 30,- Academic Staff Single copies Available at Bouw Shop (BK) for : € 5,- Students €10,- Academic Staff , PhD Students and alumni

Praktijkvereiniging BouT Room 02.West.090 Faculty of Architecture, TU Delft Julianalaan 134 2628 BL Delft The Netherlands

Sponsors Praktijkvereniging BouT is looking for sponsors. Sponsors make activities possible such as study trips, symposia, case studies, advertisements on Rumoer, lectures and much more.

www.praktijkverenigingbout.nl rumoer@praktijkverenigingbout.nl instagram: @bout_tud

For more info contact BouT: info@praktijkverenigingbout.nl If you are interested in BouT’s sponsor packages, send an e-mail to: finances@praktijkverenigingBouT.nl

Interested to join?

The Rumoer Committee is open to all students. Are you a creative student that wants to learn first about the latest achievements of TU Delft and Building Technology industry? Come join us at our weekly meeting or email us @ rumoer@praktijkverenigingbout.nl

Disclaimer The editors do not take any responsibility for the photos and texts that are displayed in the magazine. Images may not be used in other media without permission of the original owner. The editors reserve the right to shorten or refuse publication without prior notification.

CONTENT 06

Systemic Integration of Urban Farming into Urban Metabolisms -Eren Gözde Anıl TU Delft Graduate Article

16

Data and AI in Core Studio -Jeroen Janssen Core Studio Company Article

24

Sageglass -Raimond Starmans Saint Gobain Project Article

30

Superworld - Architects of Conditions -Thomas K rall and Maxime Cunin Superworld Company Article

38

AiDAPT Lab - Dr Charalampos Andriotis, Dr Seyran K hademi, AiDAPT Lab Academic Article

44

The Participatory Design Game for Social Housing in Manaus, Brazil -Vasilka Espinosa TU Delft Graduate Article

50

Deep Learning Based Structural Bridge Design -Sophia K uhn, Dr.Michael K raus, Prof.Dr.Walter K aufmann, ETH Zurich Academic Article

56

The Augmented Architect -LINK Arkitektur Company Article

64

Machine Learning for the Built Environment -Raffaele Di Carlo OMRT Company Article

70

Integrated Bio-Inspired Design by AI - Namrata Baruah TU Delft Graduate Article

78

AI in Design - Patrick Janssen and Maarten Mathof Packhunt Company Article

84

Core Studio - Adalberto De Paula and Patrick Ullmer TU Delft Academic Article

94 96

Bout Events and Trips -Praktijkvereniging Bout TU Delft

28th Board Signing Off -Lotte K at Praktijkvereniging Bout TU Delft

Editorial

EDITORIAL Dear Reader, It is with great pleasure and the highest enthusiam that I present my last edition of Rumoer as the editor-in-chief. I am honoured to have had the opportunity to work with an extremely talented editorial team. The team showed real

Alina

Aneesha

Bryan

Fieke

Pavan

Pranay

Ramya

Shreya

faith in me through the past year and helped me improve the work we do at Rumoer. I would also like to express my gratitude to all the sponsors, contributors and companies who have colaborated with us in the past year. I hope we continue to expand our circle to learn more through the next editions.

Rumoer committee 2022-2023

In this issue, we will explore the potential of data-driven I would like to congratulate and wish the best of luck to

architecture to optimize building performance, enhance

Ramya Kumaraswamy to continue and improve in her

sustainability, and improve user experience. We will

tenure what the last 28 editors-in-chiefs have been

examine how machine learning algorithms can help

working on . Having worked with Ramya over the past

architects and designers to make informed decisions

academic year, I believe she has the skills and the ability

based on real-time data, enabling them to create more

to make the Rumoer bigger and further work towards

responsive and adaptable buildings. Decisions taken at

improving the quality of the editions to come. I wish her

any phase of the design can influence countless other

the best of luck for the same.

aspects of the design and consequently becomes a task of balancing the benefits and compromise of decisions.

The past year has been big on Artificial intelligence and we are thrilled to announce our upcoming issue on

I invite all the readers to join us on this exciting journey of

the topic of Data and its impact on the architecture and

exploration and discovery as we delve into the fascinating

built environment industry. With the rapid advancement

world of data-driven architecture, AI, and machine

of

learning in the built environment.

technology,

data-driven

approaches,

artificial

intelligence, and machine learning are transforming the

Pranay Prakash Swati Khanchandani

way we design, construct and operate buildings.

Editor-in-chief | Rumoer 2022-2023

5

Food Production as an Attraction , © Eren Gözde Anıl

SYSTEMATIC INTEGRATION OF URBAN FARMING INTO URBAN METABOLISMS Eren Gözde Anıl This Building Technology (TU Delft) Master Science Thesis research investigates how to integrate urban farming into cities by utilising under-used spaces and existing waste sources via using a decisionmaking tool. It is a tool aimed to make data-driven correlations based on different urban farming systems, their inputs, outputs and system characteristics, and typical urban waste types, product types, and their interrelations, in order to provide a Decision Support System.

Modern day cities are the consumers of natural resources, as well as primary producers of waste and greenhouse gases. However, the supply of resources and the handling of the city's wastes are not sustainable processes, and are vulnerable to fluctuations in the larger external and infrastructure services. Eren's student work is a response to this challenge.

82| Data

Space

Waste

Supplement

Medium

Growing Technique

Design Characteristic

Rooftop

Food Waste

Fertilser

Soil

Compost

Facade

Coffee Waste

Nutrient Solution

Water

Intermediate Floor

Other Waste

Calcium

Ground Floor

CO2

Lime Bath*

Basement

Main Product

Bi-Product

Fish Tank

System Type Food Production

Small Crops

Heat

Spawning

Tank

Supplementary

Large Crops

Food Waste

Fish Tank Water

Aquaculture

Stacked System

Food Producing Supplementary

Mushrooms

Spent Mushroom Substrate

Air

Raised Beds

Horizontal

Worms

Fertiliser

Rainwater

Food Waste

NFT

Vertical

Fish

Fish Tank Water

Heat

Coffee Waste

Aeroponics

Modular Frame

Other Waste

EBB & Flow

Clay Balls

Gravity Trickle Water Culture

* Lime Bath is used for pasteurization of substrate.

Vermiculture

Mushroom

Plant Factory

Aquaculture

Raised Bed

Aeroponics

Hydroponic - NFT

Hydroponic - Water Culture

Hydroponic - Media Bed

Fig. 1: Urban Farming Systems (Input, System, Output) © Eren Gözde Anıl

8

Input - System - Output

producing 50% of waste and emitting 75% of greenhouse

The decision-making tool is designed to guide the

gases globally (Tsui et al., 2021). Demands of the city

designers towards the optimal options depending on

are supplied externally resulting in a dependent system

a set of variables and the relationship between these

that is not resilient to fluctuations. Demands and waste

variables. These variables are product types, byproducts,

flows of cities follow a linear process line resulting in

production method, production characteristics, inputs to

high GHG emissions, making the 2050 goals (European

the system, vacant spaces, their locations, load-bearing

Commission, 2020)unachievable if a change in the design

capacity of the supporting structures, solar exposure

of urban metabolism is not made. Integrating urban

of the spaces, their distance to existing waste flows,

farming as part of urban metabolism has the potential to

their distance to vacant spaces and demands given by

close the loop of some organic materials and resources

stakeholders.

Academic

Cities consume 60-80% of natural resources while

within the urban environment however vast amounts of waste types and urban farming systems complicate the decision-making process.

As a response to this challenge, this research investigates how to integrate urban farming into cities by utilising under-used spaces and existing waste sources via using a decision-making tool. The design rules and the methodology are formed based on a literature review regarding different farming systems, varying waste flows and computational approaches. A prototype of the tool is generated and tested on case studies in order to showcase the potential of such an approach combining food production with waste management.

First of all, in order to formulate the decision-making rules an extensive literature review, investigating 9 different urban farming systems, their inputs, outputs and system characteristics in addition to 6 waste types, 4 main product types, and the interrelation between these aspects, is conducted. A database of these inputs, systems and outputs is produced and findings are formulated into step-by-step decision-making rules (Figure 1).

Fig. 2: Inter-relation of Variables © Eren Gözde Anıl

9

82| Data

As illustrated in Figure 2, the selection of urban farming

Waste Output Point 1 :

systems is dependent on these variables and their

WO1 :{ location: (17,28,0), tag: WO1, building: BK, size:

interrelations. The selection may seem rather simple

3000, type: sawdust, node: waste}}

for one vacant space and a set of existing conditions, however, there might be multiple vacant spaces. The

The last dataset needed for decision-making is vacant

multitude of available spaces complicates the design

spaces. For every vacant space in the case study, its

problem as it introduces the possibility to build a

location, the building it is situated in, how big it is, its

network of urban farming systems serving each other.

structural capacity and solar exposure is used in the next

Consequently, combinations of urban farming systems

stage.

depend on a range of circumstances including the distance between waste output and vacant space which can utilize

Vacant Space 1:

the waste. These interrelations and circumstances are

{ V1 :{ location: (15,20,0), tag: V1, building: BK, size: 3,

translated into design rules and used for step-by-step

structure: 3, solar:1, node: vacant}}

decision-making in later stages.

It should be noted that for research purposes, waste Data Flow

quantities, the structural capacity of spaces, system

After collecting the necessary data, they should be

weights, solar exposure and solar demands are divided

represented in an accessible manner for the upcoming

into ranges starting from 1 (low) to 3 (high).

steps. For each urban farming system data regarding what

After preparing the datasets, information is processed,

kind of supplementary resources are needed, system’s

used for step by step decision making (Figure 3), outputs

weight, solar demand, inputs and outputs are stored in

are analysed and visually illustrated.

the following way: Food Cycle Vermicompost :

Even though the decision-making tool is composed of a

{ UF1 :{ type: Supplementary, weight: 3, solar demand:

set of technical reasonings, the interaction between the

1, inputs: [food waste, rainwater,sawdust, paper],

designer and the tool has a significant value. The tool

supplement: None, output: [fish food, fertiliser]}}

should be easy to operate and flexible for various project parameters. The methodology and the prototype created

10

Similarly, for each waste output source in the case study

within the scope of this research can run in the background

its coordinates, which building it is located in, the quantity

to make decisions. To make the decision making clear and

of waste and type of waste are stored in the following

less intimidating, a representative user interface- called

format.

Foodcyle- is designed.

Academic 1

2

3

4

5

6

Fig. 3: Step-by-Step Decision Making:

7

© Eren Gözde Anıl

- 1. Collect data - 2. Determine potential food-producing urban farming systems for each vacant space based on space characteristics - 3. For each space and each potential system, look for available waste. If demands are fulfilled assign the system - 4. Assign food-producing supplementary systems based on demand for the supplements and availability of resources - 5. Assign supplementary systems based on demand for the supplements and availability of resources - 6. If there are still vacant spaces available increase the search radius and repeat the previous steps - 7. Visualise the results

11

82| Data

The user interface is simply used to translate designer

Case Study (TU Delft) Data & Findings

demands to design parameters. There are mainly 5

To test the prototype (FoodCycle), TU Delft Campus is

panels in the interface: Data Input, Design, Analysis,

used as the primary case study. 145 vacant spaces and 86

Customisation and Adaptability. The project-specific

waste sources on the campus are mapped and necessary

design rules are set by filling out a design questionnaire

calculations are made to determine the waste quantities

followed by interactive design panels. (Figure 4). In the

and other essential aspects. For this case study, a search

end, the suggestions made by Foodcycle can be altered

radius, the maximum distance between vacant space and

by the user. And after all the decisions are set a breakdown

waste source, of 100 m, 200 m and 500 m are used.

of quantifiable results is represented.

Fig. 4: The Foodcycle Program Interface © Eren Gözde Anıl

12

At the end of all 4 stages, 125 vacant spaces are used

difference between these is the gradually increased

for farming purposes reaching a total of 22.18 hectares.

search radius. During the first 3 stages, a system is

The most assigned urban farming system is NFTs with

assigned only if a minimum of one resource is found, with a

62 farming locations while raised beds are second on

maximum of 2 missing non-critical resources. Foodcycle

the list with 40 locations due to the lightweight of NFTs

is run 3 times while the search radius is increased step by

and limited waste demands of raised beds. Other than

step. It is observed that not all the spaces are occupied.

these, 9 vermicomposting areas, 2 aquaculture spaces,

Therefore to illustrate the food production potential of

9 mushroom production areas and 1 plant factory are

the campus, stage 4 design rules are applied. In stage

suggested (Figure 5).

Academic

Decision-making is divided into 3 stages and the only

4, Foodcycle looks for at least one found resource first.

If there is a found item and if the missing resources are

By covering almost 22 hectares of the campus, enough

not critical for that system’s productivity, the system gets

vegetables for 88% of the Delft population can be produced

assigned to the vacant space. And the decision-making

at TU Delft Campus. These numbers are based on daily

tool runs again to assign food-producing supplementary

fruit and vegetable consumption recommendations by

and supplementary systems. Lastly, as there were still

the government. Also, while producing food, more than

unoccupied spaces left, the rules are eased even more

50% of waste of each waste type, except sawdust, can

and a system is assigned even if there are no found

be used on campus as shown in Figure 6. Lastly, more

resources based on the number of missing items. After

productive greenery can be incorporated into the campus

this stage, the yields, system decisions and used waste

encouraging people’s engagement with food production.

quantities are calculated.

Fig. 5: TU Delft Campus Stage 4 System Decisions © Eren Gözde Anıl

13

82| Data Fig. 6: TU Delft Campus Stage 4 Breakdown of Used Waste w © Eren Gözde Anıl

Conclusion

become outdated in the future, the methodology remains

Foodcycle is designed to choose an urban farming system

valid. And the same methodology can be used in the

based on existing conditions such as waste flows and

background of the user interface. Currently, the prototype

underused spaces, as well as to generate a network of

gives 1 system option for each vacant space however with

farms. After testing the tool on different case studies it

further developments, it can be designed to give a set of

is concluded that Foodcycle generates a more complex

options to let the designer make an informed decision.

network with bigger data sets due to strict design rules and the availability of waste. If the tool is utilised for smaller data sets with fewer vacant spaces and fewer waste sources then the design rules need to be eased. Another strength is that the reasoning behind each decision can be tracked. From a technical point of view, even if the tools used for Foodcycle such as python and grasshopper

14

Academic

References: European Commission. (2020). 2050 long-term strategy. https://ec.europa.eu/clima/eu-action/climatestrategiestargets/2050-long-term-strategy_en Tsui, T., Peck, D., Geldermans, B., & van Timmeren, A. (2021).

The Role of Urban Manufacturing for a Circular Economy in Cities.

Sustainability, 13(1), 23. https://doi.org/10.3390/su13010023 Eren Gözde Anıl @erengozdeanil Eren is

a

designer interest and

computational

with in

a

strong

urban

farming

sustainability.

completing

her

After

bachelor’s

degree in Architecture she started building technology master's

at

TU

Delft’s

Architecture faculty. During the first year of her studies, she focused on sustainability and climate design while she discovered her fascination with computational design and coding during the second year of her degree. After graduation, as a continuation of the winning team of Urban Greenhouse she

started

Challenge, working

on

Lettus Design along with her teammates. Currently, she is working at Packhunt as a customer success agent.

15

DATA AND AI AT CORE STUDIO Jeroen Janssen Data is all around us. It has been playing an important role in our day to day lives as designers and engineers and it only seems to become ever more important as time moves along. With the advent of large language models (LLM) such as ChatGPT and tools like ChatGPT, the world of data feels to be on fire now with everyone trying to have a piece of the cake. At CORE studio, the research and innovation team at Thornton Tomasetti, data is at the centre of everything we do, ever since the incorporation of the team in 2011. From visualising embedded metadata within large BIM models, developing applications to improve our workflows and data handling, all the way to AI and machine learning applied to our daily engineering tasks.

Fig. 1: BIM model loaded into Mirar, coloured by member section size. © CORE studio

82| Data

Thornton Tomasetti has been designing buildings and

and manipulation. Two examples of mature and widely

structures for over 70 years and as such has accumulated

used tools created by the team are Mirar and Thread.

a wealth of knowledge and expertise. What if we can make

Both are web applications that easily communicate

the entire firms’ knowledge, all our previous projects and

with metadata embedded in design models and directly

all the expertise of our leadership, accessible to every

connect to various desktop software packages.

engineer in easy-to-use design applications? At CORE studio we believe this is now possible and can be achieved

Mirar is a lightweight 3D viewer that connects to Rhino or

with the help of artificial intelligence.

Revit for easy input and visualises complex 3D BIM models in the browser, interrogating individual elements and their

Visualisation of metadata

attributes and then use advanced filtering and colour-by

Data can only be put to good use as long as we make it

visual aids to highlight the embedded metadata. 3D views

insightful to the designer. Therefore, for many years CORE

with their camera position and preferred filtering options

studio has been developing tools for data visualisation

can be saved and shared with external parties such as the

Fig. 2: A custom dashboard in Thread highlighting various structural metadata. © CORE studio

18

The next crucial step is applying the correct machine

of the desktop clients installed on their machines.

learning architecture and training strategies to the right question. CORE.AI’s data scientists have been training

Thread takes that a step further, by using those 3D models

large deep learning models on massive datasets, tuning

with metadata from Mirar, creating stunning and insightful

them to the specific design task at hand and applying

data dashboards, allowing the user to really let the data

them into design engineering apps. CORE’s journey in

tell the story. The user can arrange various graphs, filters

machine learning started several years ago with an app

and user interface controls on a set of custom dashboards

called Asterisk . This web-based tool predicts an entire

and reports. The metadata within each individual element

buildings’ structural frame in a matter of seconds. The user

can then be interrogated by filtering and highlighting

input required is limited to the massing of the building, the

those elements, with colour-by properties showing up

size of the core, and whether it is an office, residential or

synchronised through all widgets on the page at the same

commercial building. Asterisk would then utilise a trained

time.

ML dataset to generate the appropriate framing geometry

Company

wider design team or clients, without the need to have any

and beam and column sizes for the entire building.

Machine learning in design CORE.AI is the specialist team within CORE studio,

The app was a huge success and has been used many

dedicated to Artificial Intelligence (AI) and Machine

times during plenty of early-stage design options and

Learning (ML), with the objective to find new ways to apply

was particularly well suited for super-tall tower design.

these novel tools to our day-to-day engineering practice.

Although showing early success, the design of real-life

Three equally important ingredients come to play when

buildings required a more flexible approach, rather than

we think about automating and accelerating our structural

the one-stop shop that Asterisk proved to be. Over time

engineering workflows with AI: Data, Machine Learning

CORE studio started to pick apart this one large model

and MLOps.

into smaller and more specialised apps, delicately tuned to specific sub-tasks such as a single timber bay design,

The first ingredient on the journey is the Data feeding

steel-braced frames, and the likes. These could then in

into any of our processes. Seven decades of engineering

turn be put to work in concert creating compositions of

buildings and projects enable us to leverage and generate

larger, more intelligent models.

massive synthetic datasets exploring every corner of the design space. In a matter of days, CORE’s physics-based

Creating those larger workflows then automatically leads

design engines can efficiently create millions of data

us to the third ingredient: Machine Learning Operations

points that would take decades to create by humans and

(MLOps) – or, how can we create an infrastructure

even months by typical commercial software packages.

for easily turning our AI experiments into lightweight

Critical here is the curation of that data ensuring it can be

microservices that can power other applications. For

contribute meaningfully to the next steps in our AI journey.

this reason, CORE studio has developed Cortex. Cortex

19

82| Data Fig. 3: Structural frame designed by Asterisk within seconds. © CORE studio

20

of inputs. This endpoint can then be shared and used in

only a few clicks, to turn their trained ML models into a

a design application put together by the computational

powerful web API that can easily be consumed by other

designer.

applications, whether that is a Python script, a web page, a Grasshopper definition, or a custom-made ShapeDiver

Expanding the earlier example for the timber bay design;

app.

the Grasshopper definition computes the span and width

Company

is a platform and infrastructure that allows the user, with

of a standard floor bay spanning between columns, Cortex brings the power of machine learning experts and

defines the loading conditions, and then calls the API

computational designers together allowing their skill set

endpoint which communicates with the trained ML model

to complement one another. The ML expert can post their

that lives on the Cortex platform. This API call returns a set

experiments on the platform, consisting of one or several

of beam sizes for the main girders, secondary joists and

ML models organised and trained with different versions of

slab thickness, which is then rendered to the correct size

parameters, all within an intuitive user interface. When the

directly in the Rhino viewport. The app can subsequently

ML expert is happy with a specific model and version, they

be packaged and shared with every engineer in the firm,

can easily create an API endpoint with a specific schema

allowing them to quickly design a typical floor bay to a

Fig. 4: Typical Timber Bay app deployed and shared on CORE studio’s Swarm platform. © CORE studio

21

82| Data

high level of detail in a matter of seconds, satisfying code

app to utilise proprietary architects’ image libraries and

standards for both strength and serviceability, including

therefore produce entirely new design options, based

floor vibrations.

on a specifically trained style and materiality. Another experiment the team has been working on is to see how

The field of machine learning is moving fast. Generative

a pre-trained LLM such as GPT-4 and Natural Language

ML is one of those fast moving and exciting fields. CORE.

Processing (NLP) can be utilised and further enhanced

AI has been experimenting with models such as DALL-E

with information from all of Thornton Tomasetti’s previous

2 and Stable Diffusion creating an app that takes a

projects’ metadata. This suddenly brings the entire firms’

simple text prompt and optionally a reference image

knowledge to all engineers’ fingertips by just a simple

as input, while generating 3d designs directly in Rhino.

text prompt.

And furthermore, the team has been looking for this

Fig. 5: The Cortex platform lets users control and deploy trained machine learning models via API endpoints © CORE studio

22

Company

The future of AEC CORE studio is very excited about the future. AI is a powerful tool for the AEC industry, but of course, with great power comes great responsibility. CORE studio’s team is comprised of architects and structural and machine learning engineers. In addition, CORE.AI’s efforts are constantly being evaluated by seasoned engineers within the wider Thornton Tomasetti team.

With those quality measures in place, combined with

Jeroen Janssen

years of experience of developing applications for data

@tt_corestudio

visualisation, manipulation and machine learning, CORE

Jeroen

studio sees a huge potential for AI to enhance the AEC

Director

industry going forward into the future. All the while,

Tomasetti and is leading

ensuring that this integration is carried out properly, and

the CORE studio team in

executed and constantly reviewed by experienced AEC

London. CORE is Thornton

professionals.

Tomasetti’s

is

an

Associate

at

Thornton

Research

Development

and

incubator,

enabling collaboration with project teams and industry colleagues to drive change and

innovation.

Jeroen

particularly focuses on the design of microclimates for urban spaces

23

SAGEGLASS Raimond Starmans, Country Director Germany and Benelux, SageGlass.

Founded in 1989, SageGlass is the pioneer of Smart Windows.

SageGlass electrochromic glass automatically tints and clears according to daylight levels to control the natural light and heat entering in buildings. Thanks to the intelligent daylight management, SageGlass does not require the installation of blinds or shades and therefore allows for more minimalist and modern building designs.

Since 2012, SageGlass has been a wholly owned subsidiary of the Saint-Gobain Group, the world leader in light and sustainable construction.

SageGlass is a unique product resulting from more than 15 years of R&D work. To date, the company offers two fairly similar products: SageGlass Classic® and SageGlass Harmony®.

Fig. 1: SageGlass used in Raiffeisenbank, Switzerland © SageGlass

82| Data Fig. 2: System overview , © SageGlass

SageGlass Classic offers four tints: clear, light, medium

levels without visual discomfort while preserving the view

and full. When the tinted states are activated, the entire

to the outside.

pane is tinted to control luminosity. In addition to these four tints, Harmony has no less than four additional tints

But how does SageGlass work?

(eight in total). Harmony is the latest solution invented by

First of all, it is important to know that SageGlass

SageGlass’ team. This revolutionary Smart Window is the

electrochromic glass works thanks to three components:

only product capable of tinting on a gradient, delivering

the insulating glass, the control material and the

targeted glare control and maximizing daylight with a

intelligence of the system.

more natural aesthetic.

- The insulating glass unit (IGU) consists of five layers of ceramic material that are coated onto a thin piece of

26

When tinted, SageGlass products can block up to 96%

glass. Applying a small electrical charge causes lithium

of solar heat, helping to optimize energy efficiency and

ions to transfer layers making the glass tint. Reversing the

thermal comfort in the building. It also allows 60% of

polarity causes the glass to clear.

daylight to pass through in its clearest state, and blocks

- The second component is Controls Hardware. This is

up to 99% of daylight in its darkest state, optimizing light

the communication hub. It has interfaces for customer

Project Fig. 3: The glass tinting process , © SageGlass

interactions and then transmits information across the

Thus, to perform in the field, electrochromic glass needs

building.

software and controls to dictate when and how it should

- The final component, the System Intelligence, takes in

tint. Using a combination of predictive and real-time

the outdoor and indoor factors such as the weather, the

inputs, the software and controls manage daylighting,

sun’s position, building orientation, location, occupancy,

glare, energy use and color rendition throughout the day.

and time of day to create optimal comfort for every

At any point you can control the glass, manually adjusting

occupant.

the settings to your desired tinting preference from a wall

27

82| Data Fig. 4: SageGlass at Schwarzsee Hostellerie in Schwarzsee. ©SageGlass

touch panel, mobile app or BMS (Building Management

Our latest study at Brownsville South Padre International

System).

Airport shows that when SageGlass is on, occupants are 3 times more satisfied with thermal comfort, 2.5 times more

SageGlass is therefore far from being a traditional glass.

satisfied with light levels and 2.5 times more satisfied

The attraction for SageGlass is not by chance. Indeed,

with glare control.

there are many advantages in the installation of this Smart

28

Window, which, let's not forget, tints automatically. Its

Electrochromic

primary function is to control the luminosity and heat

energy efficiency and help you achieve green building

glass

can

significantly

improve

entering the room. Traditional buildings use manual blinds,

certifications for buildings. Indeed, smart glass responds

which are typically down 50%-70% of the day, blocking

to changes in the environment, limiting the heat or cold

daylight and views. Smart windows solve that problem.

entering the space, and can reduce loads on HVAC

They allow natural light to flow in the building and thus

systems. It saves an average of 20% of energy.In addition,

considerably improve the comfort and well-being of the

thanks to dynamic glass, the occupants of a building

occupants who are no longer bothered by glare or heat.

continue to have a direct view of the outdoors, which

Project

clearly improve the occupant experience. Numerous studies have shown that the direct connection to nature and the provision of natural light is beneficial to the mental and physical health of building occupants. In addition, there is evidence of improved productivity in offices and classrooms, faster recovery in health care buildings, and more purchases in commercial buildings and airports.

Smart Windows provide also a best exterior aesthetics.

Raimond Starmans

Architects can design buildings without worrying about

@sage_glass

the installation of blinds or shades: they can therefore

Raimond Starmans is the

design buildings that are fully glazed, comfortable and

Country Director for Germany

beneficial to the health of the occupants.

and

The

Netherlands

for

Saint-Gobain's

SageGlass

To date, SageGlass Smart Windows has been installed in

since

of

many buildings around the world. These include Nestlé's

Based in the Euregio in the

headquarters in Vevey (Switzerland), Schneider Electric's

Netherlands, Raimond is a

new building called IntenCity and located in Grenoble

big promotor of sustainable

(France), and Powerhouse Telemark in Porsgrunn

dynamic

glass

(Norway). SageGlass is not just for offices. It can also

together

with

be installed in buildings in other industries: for example,

and

in airports (Nashville airport in the United States), in

team. He has more than 30

buildings in the hospitality sector (Marriott Hotel in

years of work experience

Geneva, Schwarzsee Hostellerie in Schwarzsee), or in

in the architectural glass

educational buildings (Ruselokka Skole in Sweden), and

industry, collaborating with

many others.

architects, consultants, main

the

end

technical

2019.

solutions his

sales support

contractors, applicators and

Sageglass continues to refine its dynamic glass: the

fabricators in major projects

teams are trying to improve the comfort and aesthetics of the glass even further.

29

SUPERWORLD Architects of Conditions Thomas Krall and Maxime Cunin.

As architects, we oftentimes propose inspiring images of alternative futures. We imagine how our cities could look like, how our homes could feel like. But we oftentimes fall short in providing the conditions for these futures to become reality. Designing the future is both a question of creating its image as well as the conditions of the future as a common goal. As architects, we thereby should not stop at the image, but craft the necessary infrastructures to translate our visions into reality.

This is precisely what we at Superworld are thriving in - the intersection of inspiring tangible futures, supported by the intangible context, and modified conditions, that can foster them. The creation of the necessary protocols, tools, contracts, and their digital or analog materializations creates a new kind of architect. An architect who designs his or her context, rather than only responding to it.We are condition architects.

Fig. 1: Negotiating Energy- Designing the processes of retrofitting, © Superworld

82| Data Fig. 2: Evaluation of existing capacity of the rooftops using roofscape , © Superworld /MVRDV

Supported spatial mediation of the roofscape

Roofs are private spaces evolving in a tight relationship

Above Rotterdam, a landscape of rooftops slowly appears

with its public environment. The question is how to

in our common consciousness. Some of these rooftops

interweave the very individual spatial agenda of each roof

are already today being used for various purposes such

and its owner with the development of sustainable futures

as creating additional living spaces, setting up communal

for the collective. RoofScape aims to build that bridge. It

food gardens, and setting up temporary cafes. Such

aggregates individual contributions to shift from the few

activities prove the vast potential for systematically

large to the many small urban interventions planning, and

intensifying urban areas, addressing some of the

what it means to support it by policy making.

city's broader development needs such as promoting

32

biodiversity, ensuring affordable housing, generating

The project proposes a data-driven method that, on one

renewable energy, and mitigating flood risks.

side evaluates the existing capacity of roofs, and on the

Company Fig. 3: Roofscape's capacity analysis , © Superworld / MVRDV

other side simulates scenarios of desired alternative

answers questions like: How high is a roof? How much

futures. In its prototype stage, RoofScape is the first self-

solar radiation does it receive? Is it in proximity to an

initiated collaboration between Superworld, MVRDV Next,

urban green corridor? This analysis is performed on the

and the Municipality of Rotterdam, a prototype of how to

scale of the entire city, allowing users to explore both a

co-create experimental tools and push the boundaries of

macro level and the micro level of individual rooftops,

the current protocols in city-making.

through multi-layered open-source data and linked to the digital twin of Rotterdam. The result of it determines

RoofScape’s capacity analysis is carried out via a

how well distinct functions, such as green roofs, solar

computational model that systematically evaluates

panels, public program or housing can be allocated onto

individual buildings and compares their properties

a single roof.

with the requirements of different rooftop programs. It

33

82| Data

Rather than coming up with definitive proposal, or a

in which citizens are at the heart of a process. And

masterplan of the entire rooftop landscape, RoofScape

digitalization will play a crucial role in making this next

envisions a playful way to generate scenarios through

step.

iterative negotiations between what is possible on each roof and what is needed for the city and neighborhood.

Functional capacities can be linked to user-defined

Augmented citizens negotiations of retrofit

targets for individual programs. Algorithms value trade

The energy transition of the already existing built

between the capacities of the rooftops and the desires of

environment is more than solely a technical issue; it is

the user, creating a variety of plausible rooftop scenarios.

also a social challenge. It means coping with existing

Each scenario is visualized in an interactive 3D city model,

ownership

which allows for qualitative evaluation by the user in

slow decision-making processes and uneven value

addition to quantitative evaluation such as housing units,

distribution of eventual retrofits. Large scale retrofitting

kWh of energy production, or m3 of water retention that is

activities are urgently needed to reach our climate targets

automatically provided by the tool.

but the decision-making process in buildings with shared

situations,

intrusive

retrofit

measures,

ownership models is challenging. In the Netherlands, If we truly aim to see private roof spaces participating

over 1.5 million homes are part of associations of co-

to collective climate transitions, one needs to critically

owners, called VvEs (Vereniging van Eigenaars), making

integrate all the different stakeholders that make up the

the decision-making process towards more sustainable

ownership fabric of a city. When we look at the wide range

buildings increasingly complex, but highly relevant.

of ownership type in the city, ranging from individuals to housing cooperatives, associations, corporate and public

Negotiating Energy is a research project prototyping new

buildings, a comprehensive strategy for rooftop activation

methods for a more inclusive energy retrofitting process

needs to engage stakeholders more than anything else. It

within home co-owner associations. The project is

needs to present potential roofscape developers with an

putting in place the scaffolding for a scalable model of

idea of spatial qualities as well as the environmental and

simulation-based retrofit measures through Augmented

economic impacts of these activations.

Negotiations. It combines a digital energy modelling system to support the human and intrinsically social

The question then becomes not only one of data, but

experience of collective decision making.

instead one about infrastructure. It is about crafting the

34

conditions for institutional and citizens initiatives to not

The project develops this approach within the real

only co-exist but complement and amplify each other.

conditions of two communities. The collective governance

It is about providing the capacities for each to become

model of a self-organized living group, where ongoing

gardeners of building processes. Fundamentally, it is

decision-making about energy as a resource is common

about creating tools for a participatory infrastructure,

practice already. The model is then tested in a more

Company Fig. 4: Augumented collective negotiations. ©Superworld

Fig. 5: Negotiating energy retrofit possibilities - Facade and windows ©Superworld

35

82| Data

regular association of co-owners in the historic center

the cultural notion of energy. It brings together the

of Amsterdam, exploring how negotiations of retrofit

architects from Superworld with knowledge institutions

measures can be driven collectively. The selected cases

TU Delft and AMS Institute and combines data-driven

we focused on monumental, protected buildings. These

and social science approaches to solve the decision

are notoriously the most difficult to retrofit – adding

bottleneck faced by VvEs regarding energy retrofitting.

another layer of complexity for homeowners who need to deal with extra constraints and approval from monumental

The project amplifies the conventional negotiation

comity – and therefore the most challenging to see clear

procedure by integrating a data-driven assessment

paths of upscaling.

of possible retrofitting measures and a collectively accessible negotiation medium for non-experts into the

Through interviews and workshops, the researchers

process. It explores on how to translate energy modelling

include the community’s preferences into a parametric

into a enhanced social experience for constructive

modelling process to guide them in their decision.

collective negotiation and decision-making.

Parametric design and energy modeling tools like grasshopper and ladybug were used to create the multi-

Deepening citizen's understanding of their retrofitting

criteria model and iterate all possible solutions, not as

possibilities

simple data assessment. The goal is to define a catalogue

knowledge aims to increase capacities within owner

of realistic and best-performing collective energy

associations for more impactful energy refurbishments.

measures (post-insulation, architectural interventions),

Ultimately, the project seeks to facilitate the energy

which the group can decide and negotiate upon. In the

transition in the built environment by proposing an

playful form of a physical card game, complex digital

intrinsically social, yet data-driven framework for truly

modelling is translated into an inclusive medium,

collective energy retrofitting.

supported

by

institutional

and

local

accessible not only to experts in retrofitting, but every citizen. It helps a community to define if they wish to insulate the facade, replace the windows or/and change

Designing conditions

their boiler, based on information like costs, carbon

These two projects are some of Superworld’s recent

savings, energy bill reduction for each measure.

probes on how, using data and tooling, we can create the conditions for sustainable futures. Both explore

36

The energy transition is about defining new protocols

the design of the object and the medium, the support

in which societal missions are pursued and facilitated

of conversations and negotiations, whether it is about

by better system design. At the intersection between

space or resources. They emphasize the importance of

strategic design, building technology and citizen science,

imagining not only the inspiring tangible futures, but also

the exploratory research Negotiating Energy aims to design

the necessary organizational and social infrastructures to

a replicable and collective retrofit approach by reframing

support that vision.

Company

Today, if we, as architects, are willing to be conditions builders for complex social, environmental, and economic challenges of our era, we must be dealing with the technologies, processes, and culture which can enable or prevent alternative inspiring futures. We need to adapt to emerging technologies, and navigate complex social, political, and economic environments. We can design our own context, rather than being restricted to responding to it. We can be conditions architects.

Thomas Krall Maxime Cunin @build.superworld Superworld is an international practice operating at the interfaces technology

of

architecture,

and

strategic

design. Our work focuses on system thinking as a framework

for

societal

transition and social change, and is guided by discovery, design and implementation.

Our approach is to explore and challenge the underlying operating systems governing the built environment and prototype

the

physical

embodiment of how would like sustainable futures.

37

AiDAPT LAB

AI lab for design, analysis and optimization in Architecture and Built Environment Dr. Charalampos Andriotis, Dr. Seyran Khademi AI FOR A SUSTAINABLE AND RESILIENT BUILT ENVIRONMENT Empowering decision-making processes of architects and engineers through AI, across different scales and life-cycle design phases of the built environment, is a key lever for the necessary sustainability transitions in the age of data and digitalization.

Fig. 1: Exploiting topology © Casper van Engelenburg

82| Data

At AiDAPT, computer vision, data science, and decision

The AiDAPT Lab is part of the TU Delft AI Labs programme

optimization methods come together through the

belonging to the university wide initiative of AI Labs. The

development of deep learning, reinforcement learning,

24 TU Delft AI Labs are committed to education, research

and uncertainty quantification frameworks that help us

and innovation in the field of AI, data and digitization.

analyze and synthesize decisions for architectural and

These labs are an embodiment of the bridge between

structural systems. This entails operation and reasoning in

research 'in' and 'with' AI, data and digitization. They

the complex spaces created by the confluence of diverse

promote cross-fertilization between talent and expertise

imagery data, noisy sensory measurements, virtual

and work to increase the impact of AI in the fields of

structural simulators, and uncertain numerical models.

natural sciences, design techniques and society.

Application themes of interest range from automatic recognition of architectural drawings in large databases

AiDAPT Lab is a BK lab connecting two departments,

and generative design recommendations (initial design

AE+T & Architecture, maintaining also strong connections

phase) to predictive intervention planning for life

in research and education with other departments and

extension, structural risk mitigation, and multi-agent

faculties, such as the Computer Science Department.

optimization of built systems (life-cycle optimization

AiDAPT is co-directed by Seyran Khademi (Architecture)

phase).

and Charalampos Andriotis (AE+T), currently involving 4 core PhD students, who work on some of the grand

Bridging fundamental and applied AI, AiDAPT aims at

challenges of the built environment, at the interface of

creating new scientific paradigms towards a more reliable

architectural design and structural engineering.

and sustainable built environment.

Directors (Dr. Charalampos Andriotis, Dr. Seyran Khademi)

Fig. 2: AiDAPT logo. © AiDAPT

40

Academic Fig. 3: Compositional thinking. © Casper van Engelenburg

Deep Image Search of Architectural Drawings

Environmental Design”, aims to incorporate computer

Casper's research is about the development of self-

vision to help designers navigate through environmental

learning algorithms that can effectively understand visual

building design. Accordingly, the following projects

as well as compositional patterns from architectural image

are framed to be conducted along her Ph.D. trajectory:

data, ultimately to equip software to become a more

(1) Building context visualization pipeline based on the

competent partner in design. He develops deep learning

big data on the environmental simulation results (e.g.

frameworks that enable us to learn low-dimensional,

daylight, view to outdoors, and noise), (2) Environmental

task-agnostic representations of architectural drawings,

spatial zoning design using computer vision, (2)

specifically focusing on deep image search, i.e., image

Intelligent environmental design tool development to be

retrieval algorithms. This research line builds a foundation

served as building performance analysts’ assistant.

for large quantitative analysis of archival and linked visual data. Besides theoretical work, his aim is to connect it to

Structural Integrity Management of Large Scale Built

the practice by enhancing Architectural-specific search

Environment Networks

engines.

Structural systems must satisfy multiple performance and functionality requirements during their life cycle. However,

Visible Context in Architectural Environmental Design

they are subjected to different degradation mechanisms

for AI-driven Building Design

due to time-dependent stressors and hazards, affecting

To decarbonize by 2050, as agreed in the Paris Climate

the aforementioned requirements and preventing the

Accord, immediate action for lowering the environmental

structural systems from functioning properly. In view of

impact of the building sector is needed. Fatemeh’s

an aging and growing built environment, intervention and

research,

data-collection strategies need to be planned efficiently

titled

“Visible

Context

in

Architectural

41

82| Data

to monitor and maintain structural integrity so that total life-cycle costs and risks are minimized. To this end, Ziead aims to develop a Deep Reinforcement Learning (DRL)-based optimized decision making framework that possesses the potential to tackle the structural integrity management problem considering different structural, functional and socioeconomic metrics.

Value of Information and Multi-Agent Learning for

Dr. Charalampos Andriotis

Dr. Seyran Khademi

Casper van Engelenburg

Fatemeh Mostafavi

Prateek Bhustali

Ziead Metwally

Structural Life-Cycle Extension Structures

in

the

built

environment

continuously

deteriorate and make inspection and maintenance an integral part of their life cycle. Within this realm, Prateek’s research centers on fast and efficient computation of optimal maintenance plans for prolonging the life of structures to harness its downstream socioeconomic benefits. To achieve this, I am interested in leveraging deep reinforcement learning, a machine learning paradigm designed to learn optimal plans via experience, to devise plans that encompass constraints and tackle real world challenges, whilst also being reliable and explainable.

42

0

0

0

0

0

0

0

1

1

1

0

0

0

1

1

1

0

0

0

0

0

0

0

1

1

1

1

1

1

0

0

0

0

0

1

1

1

1

0

0

0

1

1

0

0

0

1

1

0

0

1

1

0

0

0

1

1

1

1

0

0

0

0

0

1

1

1

1

1

1

0

0

0

0

0

0

0

1

1

1

1

1

1

1

1

0

0

0

0

0

0

0

0

0

1

1

1

1

1

1

1

1

1

1

0

0

0

0

0

0

0

0

0

0

1

1

1

1

1

1

0

0

1 1 1 ’, 1 ( 1‘BUILDING 0 0 0 0 0 m 1i n d s1 ) ; 1 1 1

symposium

Annual Bout Symposium

1

1

1

1

{ A I & C O M P U TAT I O N A L D E S I G N I N A R C H I T E C T U R E }

0

0

1

1

1

0

0

0

0

0

0

0

ft. Industry Leaders & Distinguished Researchers from

0

0

0

1

1

1

1

1

1

1

0

0

0

0

0

0

0

82| Data 44

Fig. 1: Game Piece configurations.

A PARTICIPATORY DESIGN GAME FOR SOCIAL HOUSING IN MANAUS, BRAZIL Vasilka Espinosa, TU Delft Manaus is a city located in the north of Brazil, marked by two periods of strong economic growth in successive centuries, which led to rapid population growth and high demand for homes. Unfortunately, the formal market did not attend to the increasing housing demand of the population, especially among lower-income families, who had to resort to slums, squatters, and informal settlements. During this period, migrants from different backgrounds came to Manaus looking for job opportunities. Among them could be identified indigenous people from different ethnicity, traditional people from the Amazon, and people from other states.

Mentors Dr. Ir. Pirouz Nourian Dr. Bruno Amaral de Andrade Ir. Shervin Azadi

82| Data

Problem assesment The Government implemented housing policies with the goal to provide low-income residents with access to adequate and regular housing. For that reason, Manaus has the biggest social housing complex ever made in Brazil, with almost 9.000 housing units. Despite the initiative to build this complex, the residents did not participate during the design process, and just two housing configurations was built. Those houses could not fulfil the users’ requirements since they can differ between dwellers in an objective and subjective matter.

Each family has a different understanding of the social logic of space and how space is configured. Some families, which eventually lived in this complex, struggled

Fig. 2: User-built housing renovation to existing social housing.

to adapt to the new housing, leading them to sublet and return to live in their previous houses or build illegal and inadequate extensions of their houses to fulfil their spatial needs.

highlighting what must be changeable to be suitable for any particular context.

Research Framework

46

Being born and raised in Manaus allowed me to follow

The post-occupancy renovation process mentioned

the transformation and impacts that the city went through

earlier highlights a couple of gaps in the mass housing

during the construction of this housing complex. This

production, like lack of fulfilment of user needs, minimum

subject motivated me to think and debate ways to

or no user participation during the design process, and

allow inhabitants to custom design their homes so their

the need for renovation of the housing unit to adapt it to

social and cultural patterns of using space can prevail

the user’s preference and need. Through the literature

while keeping the construction cost down. Another

research, I could match adequate methods and concepts

question raised was how to facilitate the discussion and

to solve those problems. For example, a participatory

participation of multiple actors in the design process

design method would allow multiple actors in the design

while keeping the design process simple without the

process, and a serious game method can engage and

obstacle of technical knowledge and still ensuring a

empower people to express their requirements. While

certain level of quality. Although I used my hometown as a

mass customization, modularity and generative design

study case, similar situations exist in other countries, and

enable replicability and affordability to be achieved

it would be relevant to create a game that can adapt to any

together with meeting the individual needs of diverse

context. Therefore, I developed a meta-game framework,

users.

Graduate Fig. 3: Game Piece variation.

Other methods and concepts used to study the context and to

The literature research helped in developing design

facilitate the applicability of the game were (a) Space Syntax

principles for the game. And the result was a construction

that exams the space independently of its forms, shape,

game that is a mix of a board game with Lego pieces.

dimension, orientation, or location, instead concentrating

Each Game Piece is compatible to Lego and has different

on the topology of the space, which reflects and embody a

dimension, giving freedom to players to choose the

social pattern, incorporating collective values and the social

possible function that can represent, while the gameboard

structure of a society, (b) Cultural Values, focusing specially

represents the plot on which the housing is implemented.

in Social Values which make people create a memory of

Additionally to this, cards are used as play mechanism to

the space and feel attached to it, consequently this brings

engage the players. The game is divided into five stages

a sense of identity and belonging to a place, and (c) Open

which will order the design decision-making process

building, a bottom-up design approach which works with

from abstract to concrete.

the introduction of several levels of decision-making in the building process, and the possibility of decoupling building parts with different life cycles.

47

82| Data Fig. 4: Example translation Game Pieces to floorplan.

Game stages

another. As part of this procedure, the end-user must

Planning is the first stage where the end users will

specify where the openings will be placed and which

express and specify their criteria, preferences, program

type. The shape grammar and modular furniture can vary

of requirement, spatial organization, and social logic of

according to the Context.

the space. This stage aims to reach spatial specification, spatial relation, and collective design goals. One of the

The Evaluation phase checks the spatial quality and

outcomes will be a draft configuration made of Game

validity of the user-generated design. This stage aims to

Pieces that they are going to use as a guide for the next

inform if a design is valid or not. In case of being invalid,

stage of the game.

point out a recommendation to improve the design and return to the Zoning Stage. The criteria used to evaluate

The Zoning phase involves game mechanisms, which role

the design should be adjusted to the reality in which is

is to engage players, motivate them to work together, and

being applied, therefore it should follow the Building

induce them to discuss and make commitments. Activity

Code.

Cards used during this stage of the game are adapted to the context, since they translate the possible needs and

Shaping is the last stage, it concerns the materialization

preferences of the user, based on Context Information

of the building geometry, determining the aesthetics of

from the previous stage.

the design and concretization of the building. Different options will be available so the players can select the

48

Routing allows the end user to customize the interior

type, size, design and position of walls and openings

of each space module through a modular system of

from the wall modules. This stage must be crafted to the

furniture, allowing the visualization of hidden corridors

local architecture and style of where the building is being

and understanding of the walking flow from one space to

inserted.

Graduate

Design Game Applicability For future work and potential application, this game could be used as: (1) a learning tool to make players gain knowledge about their spatial decision and their effect on the spatial quality and validity or make them understand the quality and validity difference between different contexts, (2) a research tool to identify social and cultural patterns of using the space and its space syntax relation, (3) a computational tool that can generate automatically

Vasilka Espinosa

2D and 3D drawing, and (4) a game to be used in any

@vasilkaespinosa

other housing development.

Reflection The proposed game can only provide the right mechanism for a particular social and cultural context through (1) end-user participation, (2) context information and (3) adaptation of some of the game development processes, like Activity Cards, Evaluation, and Shape Grammar. The danger of proposing design solutions reflecting only on our views and preferences is the same of trying to fit one solution to a problem with plural solutions. Design is not neutral and is never a solitary act. Besides, design influence how people behave and live their lives. That is why is important to understand that design is an ethical activity on its own and it is fundamental to consider the complexities of the user, such as its background, pattern of habitation, spatial values, and social logic of the space.

Vasilka Espinosa graduated in Architecture and Urbanism from UFAM in Brazil, followed by a Master's in Building Technology

from

the

TU

Delft. Vasilka believes that design should have a societal relevance

and

that

the

technology we have access to today should provide a better life for everyone; independent of social, economic, cultural and geographic background, gender, race and ethnicity.

Even though this is not a reality yet, she thinks we all have a responsibility to make it accessible to everyone.

49

82| Data

Fig. 1: Prototype user interface within Dynamo (Revit) developed for the project “Brücke über den Graben” in St. Gallen, Switzerland (Senn Resources AG) (Balmer, K uhn et al, 2022)

50

DEEP - LEARNING BASED STRUCTURAL BRIDGE DESIGN Sophia Kuhn, Dr. Michael Kraus, Prof. Dr. Walter Kaufmann

We investigate AI-augmented generative design, analysis and optimisation of concrete bridge structures. This research is conducted as an interdisciplinary collaboration between the Chair of Concrete Structures and Bridge Design at ETH Zurich (kfm research) and the Swiss Data Science Centre (SDSC). The project is associated within the Design ++ research initiative at ETH Zurich focusing on advancing the field of design through interdisciplinary collaboration and innovation. The interdisciplinary project team involves: Sophia Kuhn, Rafael Bischof, Vera Balmer, Dr. Michael Kraus, Dr. Luis Salamanca, Prof. Dr. Walter Kaufmann and Prof. Dr. Fernando Perez-Cruz

82| Data

As our society faces increasing demand for sustainable

expensive design alterations in later project phases.

infrastructure, the need for efficient and effective design

Emerging generative design methods facilitate design

tools is greater than ever. One promising approach is the

space exploration based on quantifiable performance

use of artificial intelligence (AI) in the conceptual design

metrics, yet remain time-consuming and computationally

stage of a construction project to allow for informed

expensive. On the other hand, pure optimisation methods

decisions based on data. Our research project focuses

ignore non-quantifiable aspects (e.g. aesthetics or

on using AI for the design, analysis and optimisation of

constructability). Recent breakthroughs in AI have

concrete bridge structures.

shown their great potential to improve this situation by transforming many industry and research fields. However,

The conceptual design stage of projects in the

adoption to the AEC has hardly been done, mainly due to

architecture,

the lack of systematised, freely accessible data and the

industry

engineering

commonly

lacks

and

construction

performance

(AEC)

evaluations

need for incorporation of in-depth domain knowledge.

but relies predominantly on the structural designer’s

At its core, our approach is based on our developed variant

expertise. However, particularly bridge design projects

of conditional variational autoencoders (cVAE) (see

involve a great complexity due to the tremendous amount

Figure 2). We generate synthetic data using parametric

of interdependent design parameters, multiple often

modelling and state-of-the-art simulation software,

conflicting objectives, and many involved stakeholders.

which we then use to train the cVAE. The advantage of

Consequently, the current design practice leads to a

the cVAE over traditional simulation software is that it

time-consuming, iterative design process, often requiring

can speed up the performance prediction based on a

Fig. 2: Conditional Variational Autoencoder (cVAE) architecture implemented within the toolbox (Balmer, Kuhn et al, 2022). x: Design feature vector,

52

y = P(x): Performance vector,ŷ: Predicted performance vector, x̂: Generated design feature vector conditioned on a chosen performance vector y, z: latent representation.

Figure 1 (previous page).

being able to generate new designs conditioned on a set of performance objectives (Inverse Design) (see Figure

Researchers from kfm research and SDSC are developing

2). The latter remains impossible with state-of-the-

a toolbox which implements the introduced approach to

art simulation software, such as finite element analysis

be used within the structural design stage of construction

software.

projects today. The software toolbox is developed

Academic

set of design parameters (Forward Design) while also

structure agnostic and can therefore be applied to any From a dataset of synthetic bridge structures, we

structure type with parametric representation. The

extract design features x such as dimensions, materials

implemented software modules are shown in the system

and loads and performances P such as load-bearing

map in Figure 3. Additional to the synthetically generated

capacity, environmental impact and cost. We then use

data set, the framework is further informed by a second

the features as inputs to the cVAE, which at the same

data base of existing bridge structures. Prior models are

time learns to (i) generate a latent representation z, (ii)

trained to extract utilised design patterns form existing

predict the performance ŷ, and (iii) reconstruct x̂ . Once

bridge structures to inform and validate the Forward and

we have trained the cVAE on synthetic data, we can use it

Inverse Design Models (Kuhn et al, 2022).

to generate new designs. To do this, we start with a set of performance objectives y, such as a desired load-bearing

Applied to a structural design project, the framework

capacity or a target cost. We then condition the cVAE on

in development allows to rapidly explore the design

these objectives and use it to generate a set of design

space and identify optimal designs that meet the given

parameters x̂ that satisfy these objectives.

performance objectives. It provides an understanding of the dependencies of parameter and performances of

A first prototype was developed for the pedestrian bridge

the high-dimensional design spaces and allows for the

“Brücke über den Graben” in St. Gallen (Switzerland)

derivation of design rules for the analysed structure types.

planned by Basler & Hoffmann AG (Balmer, Kuhn et al,

It can therefore boost key decisions of the conceptual

2022). Using Dynamo within Revit and SOFiSTiK a data

design stage of an AEC project and foster efficient,

set was generated for the real-life project situation at

economic and sustainable bridge structures.

hand. It was shown that both the forward and inverse design mappings can be learned accurately by the cVAE

However, the use of AI in bridge design has limitations.

implementation and the evaluation time is substantially

The generated designs must still be evaluated and

reduced, after an initial data generation and training

refined by a structural engineer to ensure their safety

phase. Additionally, the deep neural network model also

and feasibility. Therefore, a focus within the framework

provides partial derivatives which gives an understanding

development is the interactivity between the AI and the

of the parameter and performance sensitivities. The

structural designer. While the AI augments the analytical

prototype interface within Dynamo can be seen in the

and creative capabilities of the engineer, the structural

53

82| Data Fig. 3: Software modules and their connectivity of the toolbox in development. (DSE: Design Space Exploration, MOO: Multiple Objective Optimisation, XAI: Explainable Artificial Intelligence)

engineer should be able to steer the design process,

References:

bringing in additional information about the individual

(Balmer, K uhn et al, 2022): V. M. Balmer, S. V. Kuhn, R. Bischof,

project context and non-quantifiable or even subjective

L. Salamanca,. K aufmann, F. Perez-Cruz, and M. A. Kraus,

performance goals (e.g. constructability and aesthetics).

“Design space exploration and explanation via conditional

In conclusion, the use of AI in the conceptual design

variational autoencoders in meta- model based conceptual

stage of a bridge design project has the potential to

design of pedestrian bridges,” 2022

greatly enhance the efficiency and effectiveness of the

.

design process. Our research project primarily focuses

(K uhn et al, 2022): S. V. K uhn, R. Bischof, G. Klonaris, W.

on using cVAE architectures as meta models for analysing

Kaufmann, and M. A. K raus, “ntab0: Design priors for AI-

and optimising concrete bridge structures. However, we

augmented generative design of network tied-arch-bridges,”

furthermore apply the toolbox in the scope of research

Forum Bauinformatik 2022.

collaborations to other structural design examples (e.g. steel grid shell structures and timber frame connections), demonstrating the broader applicability of our approach and its potential to revolutionise the design process of bridge design and beyond.

54

Academic Sophia Kuhn

Dr. Michael Kraus

Prof. Dr. Walter Kaufmann

@ethzurich

@ethzurich

@ethzurich Prof.

Dr.

researcher at the Chair of

Researcher at the Chair of

the

Chair

Concrete

Concrete

Engineering

Sophia

is

a

doctoral

Structures

and

Dr.

Kraus

is

a

Structures

Senior and

Kaufmann of

is

Structural (Concrete

Bridge Design at ETH Zurich.

Bridge Design and Co-leader

Structures

and

Bridge

Her research focuses on the

of the Immersive Design Lab

Design) at the ETH Zurich. His

intersection

of

structural

of the Design++ Initiative at

research focuses on the load-

engineering

and

artificial

ETH Zurich. He researches

deformation

intelligence. Within Design

and

Scientific

structural concrete, structural

++ projects together with the

Machine Learning for the

safety evaluation of existing

Swiss Data Science Centre

built environment. His work

bridges

(SDSC) she currently works

includes

as

on the development of a

of theory-informed machine

domain aware AI Co-Pilot

and deep learning methods

projects. He additionally acts

for the conceptual design of

and

application

as board member and expert

concrete bridge structures.

with a particular focus on

consultant for the engineering

She is teaching assistant for

structural design, component

firm dsp Ingenieure & Planer

classes on Scientific Machine

verification and generative

AG. He has been successfully

Learning

and

design. In order to ensure

involved in several bridge

Concrete.

Sophia

Structural

teaches

in

the

development

their

behaviour

of

and

buildings,

as

innovative

interdisciplinary

research

well

the transfer of knowledge

design

competitions,

studied civil engineering at

into

runs

structural

engineering

the ETH Zurich and University

companies with a focus on

projects,

of California Berkeley.

industry-related applications

appraisals,

of artificial intelligence in

regarding

addition

safety evaluation and retrofit

Kuhn

practice,

activities.

to

his

he

university

and

expert particularly

the

structural

of existing bridges.

55

THE AUGMENTED ARCHITECT LINK Arkitektur ENHANCING THE ARCHITECTURAL PROCESSES WITH DATADRIVEN METHODS FOR SUSTAINABLE AND HIGH-QUALITY DESIGN The demand for sustainability and documentation in construction projects has led to unforeseen obstacles and challenges. This results in unnecessarily complex projects that struggle to meet financial requirements, schedules, and architectural quality. The risk of taking a wrong turn based on poor analysis can decide the faith of a project.

At LINK Arkitektur, we address these challenges by combining data-driven methods with an architectural mindset to create higher efficiency and quality in our projects.

Fig. 1: Visualised data for everyday use.

82| Data

Taking the right turn

worked well for our specialists, we wanted to democratize

Our process, called “The Augmented Architect,” involves

these tools in a holistic framework to make it easier for the

using digital processes and workflows based on AI

designers in LINK Arkitektur.

and automation to connect data-driven tools with the architectural design process. We have established LINK

Rewriting everything from scratch with C# code in a

IO, a computational network connecting architects,

framework allowed us to make things more fluid, faster,

computational designers, and data specialists to develop

user-friendly, and long term maintainable. We call the

a toolbox for augmented analysis. Our approach enables

framework LINK_EP, short for Early Phase. Introducing a

us to evaluate our designs throughout the entire process

few simple data classes of a Building, Room, Windows,

and create architecture with impact.

Balconies and a few other widely used objects in BIM modelling while still having the agility of “just sketching”.

Developing Building Sketcher 2.0: From Grasshopper

This ended up being implemented into plugins in Rhino and

prototyping to software development

Grasshopper which allow us to make general analyses for

Most craftsmen rely on specialized tools for their

energy and performance but also more bespoke analyses

exact tasks. In LINK Arkitektur we have embraced that

for different national requirements in Norway, Sweden,

designers work in different digital tools in different

and Denmark. You can hear more about LINK_EP at the

locations and roles. Our Norwegian colleagues make

McNeel webinar, using link number 1 in the footnotes.

their detailed design in Archicad whereas in Denmark it happens in Revit. Our landscape architects use Autocad

The implementation, which we call Building Sketcher

with a sprinkle of Sketchup. Most of our early phase

2.0, presents a user interface which allows architects

planning, volume studies and sketching are conducted

to access essential microclimate simulations, including

in Rhino. Because of the robustness, fair license costs

Sunlight Hours, Shadows, MOA (minimum outdoor area),

and the well documented API to extend the functionality,

Pedestrian Comfort, and sustainability analysis like early

we decided to develop a framework that would give the

phase LCA Guesstimating, Daylight with the Vertical

architects superpowers in Rhino3d while maintaining a

Sky Component or the 45° / 10% rule (Scandinavian

good interconnection to all of the other tools.

standards) and Radiation. Additionally, an Area Analytics module is integrated to enhance early phase massing and

Over the years we have used various simulation tools

planning. This is, of course, customized to national area

ranging from Energy Simulations in EnergyPlus, Daylight

requirements. By writing this in a modular code base,

Simulations in Radiance, Climatic studies in Ladybug

additional modules can easily be added.

and more. Creating automated calculations from various

58

disciplines inside of the visual programming tool of

We had a few projects that needed bespoke tools such

Grasshopper allowed us to setup prototypes and apply

as a view analysis and a safety analysis. After completing

our specialist knowledge on projects. While this has

and evaluating these projects, the code was generalized

Company Fig. 2: Point cloud converted into a 3D solid object model as part of the industrial PhD project.

and integrated into the LINK_EP framework. Currently

enables architects and designers to evaluate the design

our development is leading us towards interoperability

of a building throughout the early stages of the project,

through industry standards such as IFC, Speckle, JSON,

allowing for the assessment of a multitude of complex

and even automated PDF reports. This will be developed

environmental and performative factors. In short, the

in parallel with demands on the projects. With a good

Building Sketcher 2.0 represents a critical tool in the

foundation, it only requires the extra 20% to generalize a

contemporary architectural design process, offering

workflow so it’s useful for others.

architects and designers an innovative approach to the planning and design of the built environment.

As part of this development, we also created the LINK_ Dashboards plugin which can visualize data and geometry

Adaptive reuse enhanced through augmentation

directly in Rhino. This part is now shared in public and

To apply the philosophy of the augmented architect to

is downloadable for free, using link number 2 in the

the early phase design process of reuse projects, our

footnotes.

industrial PhD student, Povl Filip Sonne-Frederiksen, is developing tools that automate segmentation of building

As a parallel line of our development, a workflow to

point cloud scans in to solid object 3D models.

perform analyze Outdoor Comfort through Pedestrian

obtaining object-based models, stakeholders can make

By

Wind Comfort (PWC) analyses using CFD calculations is

informed decisions about the design and reuse of existing

also part of our toolset. This type of analysis is especially

buildings, ultimately exposing potential and reducing

relevant for our projects and clients within cold and windy

risk which can lead to less demolition and more building

climates in Scandinavia. You can see more info of this line

transformation.

of development using link number 3 in the footnotes.

Despite the advancements in point cloud segmentation Think of it like your smart watch: By exposing how active

in other fields, challenges still exist, particularly in the

you are, it nudges you into taking an extra walk. In the

AEC industry. Moreover, the large size of point cloud

same way we visualize project performances and nudge

scans in typical buildings can pose challenges in terms of

architects to take knowledge informed decisions. This

processing time and computational resources. Therefor

59

82| Data

one of the techniques that we are looking into consists of

Architext and others. These first actors are moving fast

using RGB-D images, which are regular images with an

and are producing rapid and impressive results, but as

additional map describing the distance to the camera.

always, we should ask ourselves what the use cases might be for this technology.

Using these images, a machine learning model is trained to recognize and distinguish between different objects

Plan drawings is something that architects have a ton

and elements within a scene. Those are turned in to point

of domain knowledge within and can draw and plan out

clouds using the depth component of the image which

these at a very fast pace. Is plan generation really such a

then can then be stitched together into a larger scan

crucial part to automate and what do we see could be the

representing the whole building. The following process

future use cases that build upon this. And is the quality

then isolates individual elements and simplifies the

really on par with a professional now? And what about

geometry into solid 3d objects.

next year?

This workflow will essentially simplify early phase mapping

One of the biggest challenges for AI-generated plans is

of buildings and speed up common tasks such as making

the lack of localized datasets. However, the use of graph

an inventory, the detection and deconstruction of building

mapping which for example Finch is working with can

elements as well as planning material reuse. It also lays

address this issue but creates a need for complex graphs

the foundation for the study of different scenarios as well

and rulesets. While the latest AI technology still requires

as defining a base model for stakeholders to collaborate

some manual corrections, the potential is huge. As we see

around.

it, they are particularly useful for analysis purposes since they have all the necessary relations and oftentimes are

In summary, leveraging point cloud segmentation to

tagged and ready for machine use.

obtain 3D models of existing buildings can enhance the design process and increase the rate of building reuse,

As we develop workflows, we see that having a tagged

in line with the philosophy of the augmented architect.

and ready to use floorplan is a major advantage for early

Challenges still exist, but we at LINK are excited for the

phase analysis. Use cases that we have applied this

possibilities that will come.

too is for LCA calculations where the layout of interior elements is less important the amount of each. Daylight

Automatic floor plan generation

is another analysis that gains largely from this, while the

While building on our toolkit we always try to partner up

layout is important for daylight calculations interior room

with the fastest movers in the industry and new AI tools

dimensions can be seen as a larger factor.

have been booming. One of the most prominent areas

60

of development is plan generation, there are several

By leveraging the best parts of generative neural

promising AI initiatives, such as Finch, PlanFinder,

networks, we can provide more precise analysis on very

Company Fig. 3: An overview of some of the outputs from the Building Sketcher 2.0 plugin that we developed for internal usage. To the left you can see LINK_ Dashboards, a free plugin on Food4Rhino.

simple geometry which can be quickly filled in to give a

The Augmented Architect anno 2025

more comprehensive picture of our projects. By using

When asked to predict the future in the field of architecture,

the current state of the art technology and keeping our

it’s surprising how close the “far future” is now. Prominent

tools adaptive to when the technological breakthrough

AI researchers in 2021 found it impossible for computers

happens which it will, we can futureproof what we build.

to understand jokes and understand intentions in written

61

82| Data

and oral. Now just 2 years later, we see ChatGPT being able to do all the above.

Text and code is generated with ChatGPT and photorealistic visuals with Stable Diffusion, MidJourney or Veras for Rhino/Revit. Kreo.net handles all your quantity take off from plain pdf drawings, Finch and PlanFinder generate your plan drawings, video is edited through written prompts in RunwayML, Gamma.

Mathias Sønderskov Schaltz,

app generates your slideshows, and Microsoft Teams

Jan Buthke, Povl Filip

automatically creates to-do lists and suggests follow ups

Sonne-Frederiksen, Fabian

after meetings. The most terrific and terrifying part is that

Sellberg @ LINK Arkitektur

most of the above can be done on a smartphone from your sofa with no prior domain knowledge.

LINK

Arkitektur

is

Scandinavian

a

based

For us, a bunch of architectural techies who really enjoy

architectural

office

diving into the technical aspects of machine learning, the

around 500 employees. We

most annoying part is that the time we invested in learning

specialize

something last year is already deprecated. Someone has

developments,

already made an app for that in the meanwhile.

architecture, and hospitals.

in

large

with urban

residential

Currently

around

So, think about it. As our everyday becomes smarter

employees

around

all

10

and smarter with the help of AI, so does the everyday

Scandinavia

are

attached

of the people developing that AI. Now the AI capability

half time or full time to our

is growing exponentially, and our industry has only

computational network, LINK

scratched the surface. All we can do is embrace the

IO.

of

new powers we get and remember why we choose the architectural profession: To create better conditions for

You can read more about LINK

people and planet.

IO at www.io.linkarkitektur. com

Links 1 - https://www.youtube.com/watch?v=E7LTdrvrcLQ 2 - https://www.food4rhino.com/en/app/linkdashboards 3 - https://www.youtube.com/watch?v=PnZzLU-lsOE

- also check out our geeky corner in there if you need more inspiration!

62

Meet the largest modular builder in Europe At Daiwa House Modular Europe, we build homes sustainably and swiftly. As the largest modular builder in Europe, we have over 60 years of experience, both nationally and internationally, and we make a difference every day with industrial and circular construction. A future-proof living environment makes dreams come true and we help build them. Would you like to contribute to our mission of solving the housing shortage?

Contact us at daiwahousemodular.eu or careers.daiwahousemodular.eu

MACHINE LEARNING FOR THE BUILT ENVIRONMENT Raffaele Di Carlo, OMRT As a Data Scientist, you might expect me to sing the praises of Machine Learning and unconditionally welcome our robot overlords. I won’t. Instead, I will try to give you an objective overview of the uses and applications of Machine Learning in the field of architecture and design, with a special focus on my three-year experience at OMRT. The real estate industry is not as dense with predictive methods as others, but it still stands to gain massive returns from investing in this technology.

Fig. 1: Complex Architecture; Alex G; flickr.com; https://creativecommons.org/licenses/by-nc-nd/2.0/

82| Data

The automation of workflows

an apartment floor plan to fit into any given apartment

The idea of automating creative workflows in much the

shape, provided only with the location of the windows and

same way we have other types of work is not exactly

entrance.

new. The first Generative Adversarial Network (GAN), for example, dates as far back as 2014. GANs are particularly

Deep learning - CNN and GAN

dear to designers because they are capable of generating

Convolutional

images, starting from a text, image or even tabular prompt.

extensively used in the analysis of the built environment

The popular DALL-E and Midjourney are an example of

for their ability to extract features from a single image.

this class of models, though they are specifically designed

From identifying solar panels from the satellite images of

for unrestricted creativity (which sometimes strays

roofs (such as in Roberto Castello’s study of 2019[1]) to

quite far away from the text prompt). GANs can also be

the detection of built environment health effects provided

developed to follow stricter rules, such as being bound to

by Xiaohe Yue in 2022[2], there is no doubt that CNNs

a specific region of space or to a specific colour palette,

can play a decisive role in the development of future

to address design problems. One remarkable example of

living spaces that are both functional and aesthetically

this approach is the excellent work of Stanislas Chaillou

pleasing.

and his ArchiGAN model of 2019[4], capable of designing

Fig. 4: Stanislas Chaillou’s ArchiGAN model (2019)

66

Neural

Networks

(CNNs)

are

also

it comes to 3D reconstruction. Point-E[3], developed

by OpenAI (the same developers behind ChatGPT and Dall-E), uses point clouds to generate 3D models starting from a text prompt. While of course the graphic quality

Company

clouds are one of the most helpful technologies when

of the output still has much room for improvement, it is easy to see how such a tool could see intensive use by architects and designers once brought to a higher level of performance.

Fig. 3: An example of a GAN model architecture

Other significant factors for Machine Learning in the built environment

The basic idea behind GANs and CNNs is very simple:

Convolutional and 3D models, however, are not the only

images are matrices of pixels, and the position of each

tool at our disposal. There is more to architecture than

pixel tells us something about the nature of the object

images. Especially when developing large real estate

represented in the image. By passing a small filter over

projects, one must take into account numerous other

this matrix, we can extract numerical values which mean

factors, such as financial feasibility, environmental impact

nothing to human beings but can be used by the algorithm

and sustainability.

to determine the relationship between each group of

At OMRT, we are experimenting with predicting these

pixels. In the case of a CNN, these values are used to

factors through the power of Machine Learning. One of

calculate the probability that the image belongs to one

our experimental prototypes, Ocelot, predicts the price

of the predefined categories - for example, “cat” or “not

of real estate by category and location for up to 5 years

cat”. In a GAN, they are used to repeatedly generate new

into the future with a 10% margin of error. Stakeholders

pictures, which are then compared to the ground truth by

in most of our projects have an interest in knowing this

a classifier. If the imitation is good enough, the training

as it allows them to anticipate costs and profits early in

is complete; otherwise, a new cycle is run with slightly

the development process. We are currently also working

different parameters.

on a new model to synthesise Daylight data (very timeconsuming to calculate) from Skyview results (which

3D-based Machine Learning

are quick and easy), Horus. These models rely on more

There have, in recent years, been significant steps forward

“traditional” techniques, such as gradient boosting and

in our understanding of the applications of 3D based

random forest regression.

Machine Learning. After all, if we can predict 2D arrays of pixels there is no reason why we should not be able to do the same thing with 3D arrays of points. Indeed, point

67

82| Data

resources to collect all this data, developed a robust model capable of approximating the dynamics of wind physics, used all of our GPUs to train it over multiple days and produced an output similar to that of our ad hoc software, the results might still not be reliable enough from the perspective of technical norms on wind comfort.

Sometimes “close enough” is not close enough. And that’s why there are some instances in which Machine Learning simply cannot replace the precision of an engineer. So you can sleep tight tonight: your job is safe… for now.

Future prospects of OMRT This, of course, does not negate the massive advantages brought by Machine Learning in other points of our value Fig. 4: Typical Ocelot output, highlighting areas in which detached houses will be more expensive (green) or cheaper (red) © OMRT

chain. Thanks to tools like Ocelot, our real estate unit price prediction model, we will soon be able to provide

Limitations

meaningful insights into the future to our clients, which

Sounds great, right? Now let me tell you about something

was something we could only do in broad terms before.

ML can’t do. A large chunk of our resources is dedicated

And Horus, by converting Skyview analysis to Daylight

to computational fluid dynamics (CFD). In a typical CFD

predictions in a matter of seconds, will significantly cut

study, we receive some 3D models from our clients

computational costs and time for our engineers, who will

and we observe the effect of simulated wind flow from

have more time to dedicate themselves to tasks in which

different directions, to identify locations in which the wind can create disturbances or even accidents. Theoretically, it would be possible for us to automate at least some parts of this process. However, we would run into a number of limitations.

The first is the difficulty in collecting data. There is no such thing as a centralised database of wind studies, so any hope of automating CFD would first require us to run a large number of simulations to acquire the necessary data, which (as you probably already suspect) is neither fast nor cheap. Even if we decided to invest the time and

68

Fig. 5: Typical CFD output. Note the red area is where wind speed is higher. © OMRT

Company

their skills are irreplaceable, leading to a more efficient workflow and maximised performance.

Our clients expect us to deliver more than just designs.

They also require the expertise and insight of a professional to steer them in the right direction. This is a valuable task, one that deserves the full attention of an expert, who should not be ground down by endless, menial repetitive tasks. The implementation of data-driven technology at

about OMRT

Raffaele Di Carlo

OMRT has so far been and will continue to be decisive in

@OMRT

@OMRT

the years to come, when we will automate more and more

At OMRT we are enthusiastic

Raffaele is a Data Scientist

of our processes to give our and our clients’ engineers

about using computational

and

the time and insight they need to deliver astounding,

tools to make designers' lives

OMRT.

groundbreaking results.

easier. Our company is a fast

Machine Learning to make

growing startup founded by

his own and other people’s

two TU Delft alumni in 2018

lives easier. He graduated

after being disappointed by

with a BSc in Economics and

the inefficiency of the tools

Finance from the University

used in the AEC project

of Amsterdam, followed by

development. We help our

a MSc in Economics from

clients

Warwick University.

References [1] Castello, R. et al. (2019). “Deep learning in the built environment: automatic detection of rooftop solar panels using Convolutional Neural Networks” Journal of Physics: Conference Series, Volume 1343, CISBAT 2019 | Climate Resilient Cities – Energy Efficiency & Renewables in the Digital Era 4–6 September 2019, EPFL Lausanne, Switzerland

into

get

their

more

insight

projects

using

Lead He

Researcher enjoys

at

using

computational analysis and integrate tailor-made digital tools into their workflow.

[2] Hue, X. et al. (2022). “Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes” International Journal of Environmental Research and Public Health, 19.

[3] Nichol, E. et al (2022) “Point·E: A System for Generating 3D Point Clouds from Complex Prompts” https://doi.org/10.48550/ arXiv.2212.08751 [4] Stanislas Chaillou, 2019. “ArchiGAN: a Generative Stack for Apartment Building Design” https://developer.nvidia.com/blog/ archigan-generative-stack-apartment-building-design/

69

INTEGRATED BIOINSPIRED DESIGN BY AI

Using cell structure patterns to train an AI model to explore topology design ideas Namrata Baruah The construction industry around the world is growing exponentially.

Structural design is usually incorporated in the latter stages of building design. Structural design in the initial design phases can lead to more efficient and cost-effective structures. Still, practical tools should be available to the designer to deepen the design space and allow them to explore it efficiently. The current computational tools focus more on converging and optimizing a single solution rather than allowing the designer to explore design ideas. Generative AI models could solve the problem and expand the design space by learning from the data provided.

This thesis explores such a solution by training an AI model (variational Autoencoder) on an artificial dataset of 2D lattice patterns. A dataset Fig. 1: VAE Architecture © Namrata Baruah

of 6 unique 2D lattice patterns is created and used to train a simple

82| Data

VAE model. The methodology used in this thesis is to validate if an AI can aid in exploring design ideas by providing a more extensive design space with newly generated data that contain learned features from the dataset rather than through constraints set by the designers.

The output of the VAE model in this thesis is explored for possible integrations into the design process for the early stages of design. For this, the generated patterns that are distinct and unique are identified and applied to a shell structure to explore topology design ideas. The results show that the VAE model can learn features and provide a more significant design diversity than the original dataset through newly generated designs.

Fig. 2: Thesis hypothesis © Namrata Baruah

requires an initial domain and can be used to explore This thesis explores the possibilities of an AI–design

and validate ideas during the early design stages. The

integration. Still, for the methodology to be practically

literature available for topology mainly focused on

incorporated into the design process, further development

optimization rather than exploration. However, one study

of the AI model is required as well as exploring other

(Sigrid Adriaenssens, 2014) found that while optimizing a

advanced generative AI models would be beneficial.

circular shell structure, the optimized topology resembled a diatom found in nature. Nature shows us optimized

Current Scenario and thesis Workflow

geometries for lightweight structures, and cellular solids

Research shows that significant design decisions such

make up many materials in nature.

as building geometry, structure, massing, etc. - which account for more than 75% of the final product costs - are

For the scope of the thesis, the patterns of cellular solids

taken during the conceptual design phase. The tools must

were used as input data to train a generative AI model and

become more versatile and allow for exploration to aid

use it to generate new data, which can add to increasing

the designers during the conceptual design phase. AI can

the design space for the exploration of options.

provide a solution. Generative AI can detect the underlying pattern related to the input dataset and produce similar

The thesis had four main objectives to provide a workflow

content.

that can be used to explore the application of AI as a tool for generating design ideas:

The thesis focused on topology exploration as it only

72

1. Selection of cellular patterns to train the model.

Graduate Fig. 3: Research Framework © Namrata Baruah

2. Generating a dataset from the selected patterns

because of which they have been extensively used in

3. Training the VAE model

lightweight applications and additive manufacturing.

4. Creating a workflow for integrating the VAE results into the design process.

Six patterns were selected for creating the dataset. The

The research framework is shown in Fig 3.

dataset was generated using a parametric mode of the patterns, and a range of variables was determined to

Dataset generation

ensure the good quality of the data in the dataset. After

AI needs a lot of data to train. There can be many

the generation, the next step was to ensure sufficient

sources for training data. But, if the goal of the training

data quantity and pre-process the data for import into

is something specific and custom. It is better to create an

the AI model. Sufficient quantity was provided through

artificial dataset.

data augmentation through geometric transformation, and data reshaping and normalization were carried out

For the thesis, the input of the dataset would be 2D

to pre-process the data. The dataset was then split into

images. For that, I looked at lattice structures, which

a training dataset (80%) and a validation dataset (20%).

are a sub-class of cellular solids; they are also present

Training data is the data the model uses to train, and the

in 2D, meaning that all the structural information is in

validation data is used to check if the mode has been

the patterns. They have a high stiffness-to-weight ratio,

trained correctly.

73

82| Data

Training the VAE The VAE consists of an encoder, a latent space, and a decoder. The encoder compresses the input images to lower dimensionality; for the thesis, I used two convolutional 2D layers with 32 and 64 filters, respectively.

In the VAE, the latent space has a gaussian distribution added, which can be referred to as "noise" in the latent space that will then be used to generate new images by the decoder. The decoder reverses the compression of the encoder. First, the data is reshaped, and then the same number of layers as the encoder, but the filters are 64 and 32, respectively.

Fig. 4: VAE Architecture © Namrata Baruah

The loss function is vital for VAE training. Binary cross entropy was used to calculate the reconstruction loss and kullback leiber divergence. Both were then added to get the total loss of the training.

Results The

hyperparameters,

like

no

of

epochs,

latent

dimensions, batch size, learning rate, etc., were tuned during the training to reduce the loss of the model. The loss comparison of the different iterations shows that option one, with 6000 epochs, latent dimension 2, and a dense layer of 60, has the least loss.

Fig. 5: Results © Namrata Baruah

74

The workflow consists of 4 stages. Stage 1 and 2 involves

selected to move forward with the rest of the workflow.

extracting the pattern from the image to a parametric environment and creating the shell form. Stage 3 and 4

From the generated images, new patterns that were not

involves morphing the patterns onto the generated shell

part of the dataset were selected for use in the application

and performing a preliminary FEM analysis on the final

workflow.

shape.

Graduate

Due to the time constraint of the thesis, that option was

Fig. 6: VAE Results © Namrata Baruah

VAE as a design tool The thesis focused on the workflow of integrating generative AI during the conceptual stage of design for exploration. Four patterns from the generated patterns were selected, and compared with the existing dataset, it is observed that they are unique and weren't part of the dataset patterns.

Fig. 8: Application Workflow © Namrata Baruah

Fig. 7: Comparison of generated Images © Namrata Baruah

Fig. 9: Aplliction Preliminary Structural Analysis © Namrata Baruah

75

82| Data Fig. 10: Workflow of the Thesis © Namrata Baruah

Conclusion

The main conclusions and points of discussion of the

The thesis workflow can be summarised in the diagram in

thesis are as follows:

Fig 10. The steps involve inputting a selected dataset to the VAE model, selecting random samples from the latent

1. Dataset: the resolution of the images was 64 x 64,

space after training, decoding the results and importing

which resulted in output images of the exact resolution,

the results to a rhino/grasshopper workspace, and finally,

which can be difficult to extract detailed patterns from in

the topology exploration with a preliminary structural

the application workflow. Hence that can be increased of

analysis. Of all the steps, the designer controls the input

other formats of data can be used.

dataset, the selection of the random samples, and the

76

exploration and deriving conclusions from the outputs.

2. VAE architecture and training: The model used worked

But for future exploration, another essential factor could

effectively for the time frame of the thesis. However, there

be latent space interpolation.

could be more complex models, e.g., hybrid VAE or GAN,

Graduate

and higher hidden layers to get a more precise output to improve the model and reduce loss.

3. Application of AI in design: The results of the application workflow are more for comparison and of what is possible.

The latent space interpolation and data generation could become powerful tools in the design process. The thesis was a preliminary exploration of the possibilities of generative AI models in design.

Namrata Baruah @n.baruah Technology

can

help

architects

and

the

construction

industry

to

design more effectively and efficiently and create more sustainable Originally

structures. from

India,

I

currently work as a project engineer

at

OMRT

in

Amsterdam. I graduated my bachelors

in

architecture

from India in 2018. After that, I worked as an architect from two years but realized I want to gain more technical knowledge and was drawn to data-driven design. Hence I did my master's in Building Technology

and

chose

a

master's thesis topic focused on computational design and Artificial Intelligence.

77

AI IN DESIGN: A vision of a desirable future

Patrick Janssen and Maarten Mathot, Packhunt How will AI and big data change design?

When considering the future, we can be fairly confident about what it will not be. It will not be a centralized, intelligent design system running on some supercomputer somewhere and replacing human processes as they are currently performed. We can already see that the digital economy is slowly but steadily transforming the AEC industry. This transformation is resulting in different kinds of design and production processes, backed by different kinds of business models.

Predicting the future is a little trickier. Here, we speculate on a desirable future vision of the digital AEC economy, in which AI and big data play a significant role. But how?

Fig. 1: Generated using Stable Diffusion

82| Data

At the core of this vision is a decentralized network

Providers of products include all types of manufacturers

of computational solutions where no single actor is in

and fabricators of building systems and components. For

control. We envision a network of AEC stakeholders

these companies, fabrication-aware online configurators

providing automated computational solutions to one

can be used to generate customized product models

another, running on various cloud platforms.

tagged with the necessary data, or even to trigger complete file-to-factory manufacturing processes. For

"Any task that is currently conducted manually and has

both services and products, the solutions that are created

an element of repetition is a candidate for conversion

can either be interactively used by humans through

into a computational solution."

some appropriate web-based interface or called in an automated way by other upstream solutions.

These solutions involve computer processes that take input data and produce output data, in addition to other

A composable generator of building constructions

non-digital actions. However, developing such solutions

By allowing solutions to be composable, an ecosystem

from scratch would be difficult for most companies.

of solution makers can emerge where solutions can be

Therefore, we expect that various platforms will emerge

easily created by connecting existing smaller solutions.

for creating and sharing solutions. User-friendly visual

For instance, consider the possibility of a solution that

programming tools will be provided for stakeholders who

connects three other solutions: the first generates an

want to create solutions. These tools will be similar to

optimised massing for an apartment building based on

today's parametric modelling tools but will be available on

various performance criteria; the second calculates a

the web and will have more advanced support for a suite

building cost by generating a detailed BIM model; the

of web services, including various AI tools with access to

third predicts the sales prices of the apartments. The

online repositories of training data. Marketplaces will also

building massing solution could call various sub-solutions

emerge where stakeholders can freely share or buy and

to predict building performance based on existing

sell solutions using various revenue models, similar to

building data. The cost solution may call various product

'app store' models.

configurators for building systems such as walls, facades, roofs, etc. These configurators could be provided by the

80

AI in the AEC industry - services and products

fabricators of those systems and could generate detailed

Within the AEC industry, there are two key types of

BIM models together with cost information and delivery

companies: providers of services and providers of

schedules. The sale price prediction solution could use

products. Providers of services include all types of

historical sales data combined with detailed information

consultants, such as architects and engineers. These

from the BIM model and local neighbourhood to predict

companies can provide many of their services in a

the price of each apartment. For all these solutions, we

computational and highly automated form, with only non-

can easily imagine how AI and big data could be leveraged

standard cases requiring manual human intervention.

to improve their speed and reduce their error margin.

Company Fig. 2: AI tasks in the building design process © Packhunt

One area of AI that has recently attracted a lot of attention

Black box character as a concern

is deep learning, which is evolving at a rapid pace. Many

In response to the recent advancements in AI concerns

companies now offer deep learning models for working

have arisen on the speed of development and whether the

with unstructured data, such as text, images, videos, and

required amount of (ethical) control is being exercised.

audio. For example, Microsoft-owned OpenAI is behind

A major concern is the alignment of AI models; to what

the well-known GPT family of models that generate text,

extent the goals and actions of the AI align with the

as well as the family of DALL-E models that generate

intent of the human user, and how to test for this quality.

images. However, in many domains, including the AEC

This issue is a consequence of the black box nature of

domain, much of the most important data is structured

current AI, in which the user cannot interpret why or how

data. BIM models, for instance, are highly complex

the model has come to the outcomes it provides. The

examples of structured data. Developing deep learning

proposed decentralized model aims to alleviate the black

models for such data remains a significant challenge.

box problem in multiple ways.

When we asked ChatGPT about this, it explained that structured data contains complex dependencies and

How to gain trust of the users

requires domain-specific knowledge, both of which are

For solution users, the granular nature of the solutions

difficult for deep learning models to handle. Despite this,

means that the big black box of AI is avoided, and humans

research on deep learning with simple structured data,

remain firmly in the loop. Admittedly, one might argue that

such as tabular data, is making significant progress.

the big box is replaced by thousands of smaller boxes.

Additionally, advancements in the model training

However, these smaller boxes are performing much

workflow, in which specialised models are trained as

smaller tasks that can be much more easily verified and

specified versions of large (near-) general AI models

checked. As such, it also becomes much easier to develop

also show promise in resolving these limitations. We

trust in these solutions. For users, this trust will likely

can expect these challenges to be gradually overcome,

initially focus on the company providing the service. If a

enabling the tackling of problems that involve structured

well-known and respected engineering firm is providing

data.

a solution for evaluating a building’s energy performance,

81

82| Data

then the user is more likely to trust the results that are

buildings are used, which in turn will change what is

generated. But in case there are doubts, the user can also

considered ‘good’ building design. Additionally, human

test out some energy analysis solutions provided by other

socio-economic behaviour is becoming more unstable,

engineering firms, to verify that the answers are similar.

often triggered by environmental and health crises.

In this way, users are not forced into blindly accepting the

This will further intensify the need for buildings to

correctness of one AI but are instead able to take a more

adapt, eventually leading to new ways of constructing

critical stance.

buildings that enable just-in-time reconfiguration. All of this will require a never-ending supply of new types

Machine-learning as a service

of computational solutions, delivering products and

For solution makers, the composable nature of solutions

services that we cannot yet imagine.

lowers the threshold and makes the process of creating

latforms of solution makers

solutions much more accessible. For instance, instead of attempting to create their own machine-learning

The development of a platform for solution makers

algorithms, solution makers can utilize services that will

In this vision, the emergence of a rich and diverse

run machine-learning models for them with their own

ecosystem of solution makers and users is key. Within

training data. Trust in the company providing the solution

the AEC industry, we still have a long way to go. The

is again crucial. If the company has a proven track record

first step is to create platforms that support such

in developing machine learning models, then the solution

ecosystems. At Packhunt, we are working on such a

maker is more likely to trust the results' quality. Moreover,

platform. We are interested in your vision and opinions

if the company advertises its use of these trusted

on the AI development in the AEC industries, so please

machine-learning solutions, it will likely increase its own

do not hesitate to get in touch. If you would rather like to

trustworthiness.

get some hands-on experience, join our early-adopter program!

The future of smart buildings So far, we have discussed how the design and production process in AEC will change in the future. However, we also expect the buildings themselves to undergo radical changes. Firstly, the flood of live IoT data from buildings is growing exponentially. Many are still trying to figure out what to do with all this data. There are already many examples of how "smart buildings" can adapt dynamically to what is happening inside and around them, including their app-wielding human inhabitants. Lessons learned from this data will provide a unique insight in how

82

Company Maarten Mathot

Patrick Janssen

@Packhunt

@Packhunt

Maarten Mathot is Solution

Patrick

Specialist

Packhunt.

of Research at Packhunt.

is

in

He holds a PhD in design

Civil Engineering at Delft

optimisation and has over

University

Technology,

two decades of experience

and his primary interests

in academic research and

include parametric design,

education

into

experimental

software

Information

systems

development,

Knowledge-

His

at

background of

Janssen

is

performance-driven

Head

Building and

design

Based Engineering and other

at the National University

emerging technologies.

of Singapore, University of Melbourne and The Hong Kong Polytechnic University School of Design.

83

CORE STUDIO Adalberto De Paula , Patrick Ullmer and Sebastian Stripp CORE primarily focuses on Design Computation in an architectural and structural design context, with emphasis on building technologies. It addresses various scales within the built environment, ranging from urban to building products, and by paying attention to environmental, social, cultural, and ethical aspects. The design assignment of the studio invites the students to analyze an urban planning problem and develop a detailed architectural design proposal as a response to it.

They are asked to do so by developing a comprehensive understanding on the given urban context including its environmental, social, and technical aspects, and by responding to it through an elaborated architectural solution which addresses its functional, structural, and technological dimensions. Moreover, they are challenged to develop an integrated digital workflow, in which, design computation is the principal approach for modelling both the problem and the solution.

Fig. 1: alt-r external view

82| Data

The course setup involves and integrates 5 modules as

Tools, Methods, and Inspirations

follows:

This module includes a series of lectures and debates on several topics which can inspire or influence the students

Design Studio

towards the learning goals of the course. It includes

This module focuses on integrated design development.

presentations given by guest lecturers.

It aims to integrate the different disciplines in an architectural context and output an extensive project

Project: alt-r

which addresses all of the course learning objectives.

The project proposed an alternative reality for space Programming

scarcity in cities, lack of proximity to public transportation,

This module focuses on the knowledge and skills in

and centralized cities. It also questions the use of

programming, which are needed for the entire design

land and the static way of building of nowadays. The

process. It includes a series of lectures and exercises on

objectives were creating a network of user mobility and

programming (Python) fundamentals. What is presented

transportation means, creating a dynamic building system

in these sessions are principally sufficient for a successful

that can adapt to the continuously changing world,

outcome in terms of programming skills. However, It

creating a modular and reusable structure, and creating

is allowed and encouraged to go beyond the content of

a user-centered building that embraces adaptability to

the lectures and experiment with different programming

different needs.

languages, tools or methods.

The Delft Campus station was chosen for the project Materials & Structures

due to the large potential of creating a new centrality in

This module focuses on the successful integration of

Delft. As starting point, an analysis of the percentages

know-how and techniques on Building Materials and

of required functions was made to create the building's

Structural Design into their projects. It focuses on efficient

program taking into consideration the current surrounding

and environmentally friendly solutions which are needed

activities and the inhabitants' change over time.

for the proposed architectural approach.

The architectural design was based on the voxelization Design of Construction

of the space to create a base to populate the functions

This module focuses on (pre)rationalization of the design

needed. A voxel is a mathematical unit in space that later

concepts towards construction challenges. It invites the

gave place to the pods' occupation. It has a volume of

students to develop not only creative but also realistic

6.5x6.5x6m. The starting point was set up at the maximum

solutions, which use and develop efficient, sustainable

boundary height according to the structural requirements

and innovative construction technologies.

of having a structure that could handle movements of change. Then some voxel extraction was made to adapt

86

Academic Fig. 2: Design strategy of alt-r cluster

Fig. 3: Manufacturing model of alt-r

87

82| Data

to the site context, such as adapting to catch more sun in

constructed in the same way, the beams and the columns

the winter and avoid housing occupations in noisy spaces.

can then be reused easily in other locations. In addition,

The vertical circulation was placed according to fire safety

the pods themselves can be transported to another

distances and from it, the horizontal circulation was

location, which increases their flexibility and prolongs their

generated as starting point of the functions occupation.

lifespan. The loop of reusing the construction members

This placement was generated by generative aggregation

and finally the material itself is therefore endless.

rules per floor. An exact amount of each function unit was given to the algorithm to then place all of them.

Project: Hello,Boat!

The main objective for the structural design of this project was to determine the maximum height of this hub as well

The project aims to tackle the current waste management

as the optimal cross-section sizes the design a modular

situation in the center of Delft by adding a second layer

structure. The main challenge for this flexible infrastructure

of transportation to the city's infrastructure. Currently,

system is the fact that it is constantly changing/moving,

residents of Delfts center are deposing their household

and it is hard to plan for that structurally. For this reason,

trash early in the morning on specific days on the

we compared different loading scenarios of the different

curbside. Garbage trucks then maneuver through Delft's

functions and took the highest one as well as simplified

narrow streets to pick up trash bags, causing congestion

some functions to limit the loads. In addition, we also

and noise pollution. The vision of "Hello, Boat!" is an

calculated the worst-case scenario where each voxel of

autonomous fleet of boats with modular trash cans.

the hub is filled with a pod.

The residents can freely decide when and what type of waste to discard around the clock. Several hubs on the

88

Based on research we chose steel as our selected material

canal edges can be used to order a vessel arriving in less

for our structure due to its structural performance as well

than 3 minutes. Boats with full capacities will return to a

as its suitability and durability for a flexible structural

waste facility where they are emptied and maintained.

system. In addition, we selected timber for the pod due

The concept is not limited to waste management but has

to its lightweight properties, bio-based nature, and the

potential applications in cargo, passenger transportation,

healthier indoor climate it will provide to its users.

and the construction of temporary structures.

The growth of alt(r) is made possible in sections. Each

Since Delft is striving for a car-free center by 2040,

section consists of a few grids, which then make it

the utilization of Delft's canals will contribute to a less

possible to flexibly distribute different pods and different

crowded city, a reduction in noise pollution, and lead to

functions possible over the years. However, even if a part

user-centered waste management. The idea is based on

of the grid is not being used in one location anymore, the

the precedent of ROBOAT, an MIT/AMS research program

steel connections can be cut apart. Since all the hubs are

on self-driving floating vessels in metropolitan areas. The

Firstly, they had to establish a functioning network of the canals in Delft followed by designing an intervention in the historic city with a low footprint and adaptable design.

Finally, they needed to create a structurally feasible hub

Academic

team faced three challenges in developing this project.

while keeping material amount and complexity down.

The integration of computation was essential in this project in every one of the latter challenges. Data analysis formed the project's foundation, enabling the team to collect, plot, and filter databases. Using a comprehensive data set, the team was able to create rules that defined the routes, fleet of boats, and trash volume, all of which contributed to achieving a maximum waiting time of 3 minutes for the vessel. The computational workflow for data gathering, plotting, and network design was accomplished in python, using libraries such as osmnx and network X. The filtered results were then implemented in Grasshopper for Rhino to visualize the output.

The architectural and structural discipline worked with a parametric Grasshopper script. The script effectively generates hubs at virtually any location near Delfts canal edges where the physical surroundings define the boundary conditions. The structural team made use of Finite Element Methods in Python, RFEM Dublal, and Karamba to specify the dimensions, connection types, and materials for each generated hub.

The project "Hello, Boat!" demonstrated the potential of alternative transport solutions for cities with an established water network. The study showed that the concept could significantly benefit residents and the urban plan of 2040. MIT and AMS intend to put the Fig. 4: Redesign of Roboat for carrying modular containers

89

82| Data Fig. 5: City center route strategy

90

layout followed by the structural design together with the

advancements in technology and legislation.

material kit-of-parts.

In Unpack the structural approach for the delivery hubs

Unpack

differs from a traditional Grasshopper parametric design.

Instead of defining boundary conditions for the parameters,

Unpack aims to solve the package delivery problems

we defined rules and logic conditions for structural design

of Delft with an easily scalable system, while trying to

decision-making. This required using loops to iteratively

reimagine the future of shopping. Unpack is a platform

apply the rules and logic to the structural design. Loops,

technology, incorporating multiple aspects of the delivery

which are not a standard Grasshopper feature were

logistics from packaging to delivery, from urban scale

handled by the plugin Anemone. Through this approach,

logistic networks to local social networks, and from

our designs are not bound by a predefined parameter

material supply chains down to the individual design and

space, but instead develops iteratively following a set of

structure of each local delivery hub.

rules that can be added to or modified. This also means we

Academic

vessels into operation within the next 5-10 years, pending

do not predefine the possible topologies of the structure, Design Algorithms and Rules

meaning the number of columns and shear walls, instead

A crucial part of the Unpack system is the physical

the rules define the conditions for placing each structural

delivery hub placed in each neighbourhood. 100’s or

element. This allows the Unpack structural algorithm to

1000’s of these need to be designed for each city and

handle any delivery hub design of any size.

algorithms were developed to handle first the space plan

Step-by-Step Structure Explained

Fig. 6: Structural Design

91

82| Data

The structural algorithm starts with a layout of the primary

Studio Supervisors

and secondary roof beams, as well as 3 randomly placed

Charalampos Andriotis

initial columns. The optimization of the structure then

Frank Schnater

consists of 3 overarching steps: First, identify the point

Hans Hoogenboom

on the structure with the maximum displacement across

Serdar Asut

all the load cases. Second, analyse the displacement

Sevil Sariyildiz

and decide between the available structural elements;

Stijn Brancart

column, shear wall etc. while considering the available fixing points in the structure. Third, repeat if the exit

alt-r

conditions for structural stability are not met.

Adalberto de Paula Alina Wagner

The initial random columns are removed one by one as

Daniela Martinez

new optimised support locations are identified. Each step

Julia Gospodinova

can be modified independently, making for a modular

Lotte Kat

set of rules and logic that can be easily improved. For

Talal Akkaoui

the current simpler implementation only columns and shear walls are used. Additional elements such as slanted

Hello, Boat!

columns and cables were also investigated but were not

Eva Schoenmaker

included in the current kit-of-parts. The materials in the

Marialena Toiliopolous

kit-of-parts determine the structural element properties

Nathanel Tzoutzidis

such as stiffness and strength, but also the joint fixing

Patrick Ullmer

conditions and stiffness, which heavily influences how

Pranay Khanchandani

bending is transferred through the elements.

Sarah Droste

This rule-based approach was chosen because a simple

Unpack

parametric approach would limit the possibilities of the

Job de Vogel

final structural layout and it suited the open-endedness

Lisa-Marie Mueller

of the Unpack platform – ready to adapt for any logistics

Henk Rusting

network of any size.

Sebastian Stripp Jirri van den Bos

92

Academic

ALDOWA

Join our Team, Become a Façade engineer!

Email us at INfo@aldowa.nl 93

82| Data

Academic Year Event Chart December 2022 to April 2023

02-03-2023 Alumni Event

Q2

94

14-02-2023 Valentines Day

22-02-2022 Master Drinks

03-03-2023 De Groot en Visser Study Trip

BouT 08-03-2023 OMRT Lunch Lecture

22-03-2023 Concrete Workshop ?

31-03-2023 Bout Potluck

05-04-2023 Bout Study Lunch

More events coming up!

95

82| Data

2022-2023

96

BouT

Board 28 Signing Off

by Lotte Kat

As my term as the Chairperson of the Board of the Study Association for Building Technology ends, I am grateful for the experiences and opportunities this position has brought me. Being BouT's chair has been a tremendous learning opportunity for me, which has challenged me and made me develop personally and professionally. I've been honoured to work with a committed group of people that was passionate about improving our community by building technology throughout the past year.

Together, we have planned several activities and projects to enrich the educational environment and bring Building Technology students from different years together. The 28th BouT board has done a fantastic job; I'm incredibly proud of them.

First and foremost, serving as a chairman has taught me the value of cooperation and communication.

One of the main challenges of being the chairman has been managing a team of various people with different perspectives, backgrounds, and personalities. It's critical to foster a welcoming environment where everyone feels respected and inspired to support the association's objectives. I've concentrated on having open conversations, establishing expectations, and assisting fellow board members. I have learned how to collaborate with people to accomplish a common goal, listen intently and answer wisely, and create agreement and move a group ahead throughout numerous meetings. Another challenge is balancing my studies, personal life, and the role's responsibilities. The position requires significant time and effort, but I've learned to prioritize and manage my time effectively.

97

82| Data

Seeing how our activities brought students together was one of the highlights of the last year. From the pizza party to the lunch lectures to the study trips, we allowed students to interact and share knowledge. One of the highlights of my time on the board was organizing the two more significant events, Debut and Symposium. Two occasions where companies come to share their experiences and insights on different industries. Although it was a difficult task requiring careful preparation, collaboration, and attention to detail, the outcome was wonderfully fulfilling. Observing the students' enthusiasm and involvement was really motivating as they engaged with the companies and learnt more about various career paths. But more than simply the events themselves stood out. Seeing the organizing work taking place in the background was really amazing. I am appreciative of the tireless effort carried out by our committees to ensure the success of each event. Regardless of the challenges we ran through along the way, we always managed to group together and get through them.

As I hand the baton to the following board, I'm thankful for my experiences and the chance to contribute to something meaningful and worthwhile. I'm aware that the next board will face its own challenges and opportunities, but I have faith that they will rise to the occasion in the same way we did. And I'm eager to see how the organization develops and flourishes in the years to come.

98

28th BOUT BOARD

THE TEAM

Study Association - Building Technology

BouT

^ ^ ^

99

82| Data

Talal Akkaoui Chair of Company Relations This year with BouT’s 28th board has been filled with new ideas, challenges, and unforgettable memories. The Debut event was a big success for the 6th year in a row. This year 7 companies and 50 students were brought together to brainstorm solutions for climate change issues. Board 28’s mission to create more connections between 1st and 2nd year students was also a great success, a record number of 30 BouT participants for a program of 160 people reinforced this achievement. I want to thank the BouT board for making this year a blast, it was an honor to work with each of you. Finally, a special thanks to the company relations committee that always had my back. Best of luck to Board 29, onwards and upwards!

Veronique van Minkelen Chair of Events As head of events of the study association BouT, it's been an amazing year working with my event committee and the board. We started the year with a quarterly planning, organizing social events like the Master Events, Pizza Party and BouTpub, and a range of educational events such as lunch lectures and alumni events. Our flagship event, Debut, was a huge success. Now we're busy organizing the symposium and we're excited to see the results. This year, I've learned so much, from event planning to team management, and it's been great to see our hard work pay off. It's been an incredible experience setting up the Master Events with BOSS, and I'm excited to see what the future holds for BouT events.

Pranay Khanchandani Chair of Rumoer It is with great pleasure and the highest enthusiam that I present my last edition of Rumoer as the editor-in-chief. I am honoured to have had the opportunity to work with an extremely talented editorial team. The team showed real faith in me through the past year and helped me improve the work we do at Rumoer. I would also like to express my gratitude to all the sponsors, contributors and companies who have colaborated with us in the past year. I hope we continue to expand our circle to learn more through the next editions. Personally, being a part of this long lasting tradition as part of the 28th Board has been an incredible experience. I wish the best of luck to the upcoming Board for their tenure!

100

As the Chair of Media & PR, it has been a great privilege to be a member of an optimistic and enthusiastic

BouT

Samanwita Ghosh Chair of Media and Secretary team. One of my key responsibilities was designing our public image for the year, including merchandise that could represent the entire track. Through the year, I enjoyed brainstorming ideas with the Board, and although there were logistical challenges, it was fulfilling to see our collective vision come to fruition. One of the highlights of my tenure was working with a dedicated and hardworking team that brought together The Building Minds Symposium. I am grateful to Jasmine Wong, Aron Bakker, and Gargi Gokhale for their contributions. Overall, being a part of this long-standing tradition as part of the 28th Board has been an incredible experience.

Jelle Ten Hove Chair of Education During my time as BouT's Chair of Education, I have been committed to improving the quality of education for our students, advising students on academic matters, and representing the interests of BT-students in the larger BK educational context. I have been fortunate to work alongside a dedicated team of both board and committee members all sharing the same vision: strengthening the BT-community by bringing together different aspects of what make BT - the educational program, companies active in the business, relevant and urgent topics and most of all the BT-students themselves. I have truly enjoyed serving as this year's chair of Education, wishing the next BouTboard all the best in their new adventure as the 29th Board.

Zahra Mohtadi Chair of Study Trips As the chair of the study trip for BouT association, my experience has been nothing short of transformative. Leading the planning and execution of the trip has provided me with valuable leadership skills, organizational expertise, and a deep sense of accomplishment. Coordinating with the team, arranging logistics, and overseeing the trip's smooth operation has honed my ability to handle complex tasks and manage diverse personalities. Building new connections and forming meaningful relationships has been a highlight of my journey as the chair of the study trip. It has equipped me with skills, connections, and memories that will last a lifetime, and I am grateful for the chance to contribute to the success of the organization and meet amazing individuals along the way.

101

82. Data

Platinum Sponsors:

Silver Sponsors:

Bronze Sponsors:

4th quarter 2023

https://issuu.com/rumoer https://bouttudelft.nl/ rumoer@praktijkverenigingbout.nl rumoer_bt bout_tud