Rumoer 82: Data by RuMoer

periodical for the Building Technologist

82. Data

featuring OMRT,

AiDapt

A(BouT) Building Technology

Eren Gozde Anil, Namrata Baruah, ETH Zurich, Saint Gobain, Superworld, CORE Studio, Link Arkitektur, Packhunt, Vasilka Espinosa,

Lab,Bout

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.

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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 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.

I would like to congratulate and wish the best of luck to Ramya Kumaraswamy to continue and improve in her tenure what the last 28 editors-in-chiefs have been working on . Having worked with Ramya over the past academic year, I believe she has the skills and the ability to make the Rumoer bigger and further work towards improving the quality of the editions to come. I wish her the best of luck for the same.

The past year has been big on Artificial intelligence and we are thrilled to announce our upcoming issue on the topic of Data and its impact on the architecture and built environment industry. With the rapid advancement of technology, data-driven approaches, artificial intelligence, and machine learning are transforming the way we design, construct and operate buildings.

In this issue, we will explore the potential of data-driven architecture to optimize building performance, enhance sustainability, and improve user experience. We will examine how machine learning algorithms can help architects and designers to make informed decisions based on real-time data, enabling them to create more responsive and adaptable buildings. Decisions taken at any phase of the design can influence countless other aspects of the design and consequently becomes a task of balancing the benefits and compromise of decisions.

I invite all the readers to join us on this exciting journey of exploration and discovery as we delve into the fascinating world of data-driven architecture, AI, and machine learning in the built environment.

Pranay Prakash Swati Khanchandani Editor-in-chief | Rumoer 2022-2023

Rumoer committee 2022-2023 Alina Aneesha Bryan Fieke

Editorial

Pavan Ramya Shreya Pranay

Food Production as an Attraction , © Eren

Anıl

Gözde

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.

Heat

* Lime Bath is used for pasteurization of substrate.

Vermiculture

Mushroom

Raised Bed Aquaculture

Hydroponic - NFT

Hydroponic - Water Culture

EBB & Flow Gravity Trickle Water Culture

Plant Factory

Aeroponics

Hydroponic - Media Bed

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System Type Bi-Product Main

Medium Supplement Waste Space

Growing Technique Design Characteristic

Product

Roo op Facade Intermediate Floor Ground Floor Basement Food Waste Co ee Waste Fertilser Nutrient Solution Other Waste Food Waste Co ee Waste Other Waste Clay Balls

CO2 Calcium Lime Bath* Soil Water Fish Tank Water Air Aeroponics

Raised Beds Compost Spawning NFT

Aquaculture Horizontal Fish Tank Tank Food Production Supplementary Food Producing Supplementary Vertical Modular Frame Stacked System

Worms Small Crops Large Crops Fish Mushrooms

Fertiliser Heat Food Waste Fish Tank Water Spent Mushroom Substrate Rainwater

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

Cities consume 60-80% of natural resources while producing 50% of waste and emitting 75% of greenhouse gases globally (Tsui et al., 2021). Demands of the city are supplied externally resulting in a dependent system that is not resilient to fluctuations. Demands and waste flows of cities follow a linear process line resulting in high GHG emissions, making the 2050 goals (European Commission, 2020)unachievable if a change in the design of urban metabolism is not made. Integrating urban farming as part of urban metabolism has the potential to close the loop of some organic materials and resources 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)

Input - System - Output

The decision-making tool is designed to guide the designers towards the optimal options depending on a set of variables and the relationship between these variables. These variables are product types, byproducts, production method, production characteristics, inputs to the system, vacant spaces, their locations, load-bearing capacity of the supporting structures, solar exposure of the spaces, their distance to existing waste flows, their distance to vacant spaces and demands given by stakeholders.

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

As illustrated in Figure 2, the selection of urban farming systems is dependent on these variables and their interrelations. The selection may seem rather simple for one vacant space and a set of existing conditions, however, there might be multiple vacant spaces. The multitude of available spaces complicates the design problem as it introduces the possibility to build a network of urban farming systems serving each other. Consequently, combinations of urban farming systems depend on a range of circumstances including the distance between waste output and vacant space which can utilize the waste. These interrelations and circumstances are translated into design rules and used for step-by-step decision-making in later stages.

Data Flow

After collecting the necessary data, they should be represented in an accessible manner for the upcoming steps. For each urban farming system data regarding what kind of supplementary resources are needed, system’s weight, solar demand, inputs and outputs are stored in the following way:

Vermicompost :

{ UF1 :{ type: Supplementary, weight: 3, solar demand: 1, inputs: [food waste, rainwater,sawdust, paper], supplement: None, output: [fish food, fertiliser]}}

Similarly, for each waste output source in the case study its coordinates, which building it is located in, the quantity of waste and type of waste are stored in the following format.

Waste Output Point 1 : WO1 :{ location: (17,28,0), tag: WO1, building: BK, size: 3000, type: sawdust, node: waste}}

The last dataset needed for decision-making is vacant spaces. For every vacant space in the case study, its location, the building it is situated in, how big it is, its structural capacity and solar exposure is used in the next stage.

Vacant Space 1: { V1 :{ location: (15,20,0), tag: V1, building: BK, size: 3, structure: 3, solar:1, node: vacant}}

It should be noted that for research purposes, waste quantities, the structural capacity of spaces, system weights, solar exposure and solar demands are divided into ranges starting from 1 (low) to 3 (high).

After preparing the datasets, information is processed, used for step by step decision making (Figure 3), outputs are analysed and visually illustrated.

Food Cycle

Even though the decision-making tool is composed of a set of technical reasonings, the interaction between the designer and the tool has a significant value. The tool should be easy to operate and flexible for various project parameters. The methodology and the prototype created within the scope of this research can run in the background to make decisions. To make the decision making clear and less intimidating, a representative user interface- called Foodcyle- is designed.

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- 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

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

1 4 7 2 5 3 6

7. Visualise the results

The user interface is simply used to translate designer demands to design parameters. There are mainly 5 panels in the interface: Data Input, Design, Analysis, Customisation and Adaptability. The project-specific design rules are set by filling out a design questionnaire followed by interactive design panels. (Figure 4). In the end, the suggestions made by Foodcycle can be altered by the user. And after all the decisions are set a breakdown of quantifiable results is represented.

Case Study (TU Delft) Data & Findings

To test the prototype (FoodCycle), TU Delft Campus is used as the primary case study. 145 vacant spaces and 86 waste sources on the campus are mapped and necessary calculations are made to determine the waste quantities and other essential aspects. For this case study, a search radius, the maximum distance between vacant space and waste source, of 100 m, 200 m and 500 m are used.

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

Decision-making is divided into 3 stages and the only difference between these is the gradually increased search radius. During the first 3 stages, a system is assigned only if a minimum of one resource is found, with a maximum of 2 missing non-critical resources. Foodcycle is run 3 times while the search radius is increased step by step. It is observed that not all the spaces are occupied. Therefore to illustrate the food production potential of the campus, stage 4 design rules are applied. In stage 4, Foodcycle looks for at least one found resource first. If there is a found item and if the missing resources are not critical for that system’s productivity, the system gets assigned to the vacant space. And the decision-making tool runs again to assign food-producing supplementary and supplementary systems. Lastly, as there were still unoccupied spaces left, the rules are eased even more and a system is assigned even if there are no found resources based on the number of missing items. After this stage, the yields, system decisions and used waste quantities are calculated.

At the end of all 4 stages, 125 vacant spaces are used for farming purposes reaching a total of 22.18 hectares. The most assigned urban farming system is NFTs with 62 farming locations while raised beds are second on the list with 40 locations due to the lightweight of NFTs and limited waste demands of raised beds. Other than these, 9 vermicomposting areas, 2 aquaculture spaces, 9 mushroom production areas and 1 plant factory are suggested (Figure 5).

By covering almost 22 hectares of the campus, enough vegetables for 88% of the Delft population can be produced at TU Delft Campus. These numbers are based on daily fruit and vegetable consumption recommendations by the government. Also, while producing food, more than 50% of waste of each waste type, except sawdust, can be used on campus as shown in Figure 6. Lastly, more productive greenery can be incorporated into the campus encouraging people’s engagement with food production.

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

Conclusion

Foodcycle is designed to choose an urban farming system based on existing conditions such as waste flows and underused spaces, as well as to generate a network of farms. After testing the tool on different case studies it is concluded that Foodcycle generates a more complex 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

become outdated in the future, the methodology remains valid. And the same methodology can be used in the background of the user interface. Currently, the prototype gives 1 system option for each vacant space however with further developments, it can be designed to give a set of options to let the designer make an informed decision.

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

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 is a computational designer with a strong interest in urban farming and sustainability. After completing her 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 Challenge, she started working on Lettus Design along with her teammates. Currently, she is working at Packhunt as a customer success agent.

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Eren Gözde Anıl @erengozdeanil

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

Thornton Tomasetti has been designing buildings and structures for over 70 years and as such has accumulated a wealth of knowledge and expertise. What if we can make the entire firms’ knowledge, all our previous projects and all the expertise of our leadership, accessible to every engineer in easy-to-use design applications? At CORE studio we believe this is now possible and can be achieved with the help of artificial intelligence.

Visualisation of metadata

Data can only be put to good use as long as we make it insightful to the designer. Therefore, for many years CORE studio has been developing tools for data visualisation

and manipulation. Two examples of mature and widely used tools created by the team are Mirar and Thread. Both are web applications that easily communicate with metadata embedded in design models and directly connect to various desktop software packages.

Mirar is a lightweight 3D viewer that connects to Rhino or Revit for easy input and visualises complex 3D BIM models in the browser, interrogating individual elements and their attributes and then use advanced filtering and colour-by visual aids to highlight the embedded metadata. 3D views with their camera position and preferred filtering options can be saved and shared with external parties such as the

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

wider design team or clients, without the need to have any of the desktop clients installed on their machines.

Thread takes that a step further, by using those 3D models with metadata from Mirar, creating stunning and insightful data dashboards, allowing the user to really let the data tell the story. The user can arrange various graphs, filters and user interface controls on a set of custom dashboards and reports. The metadata within each individual element can then be interrogated by filtering and highlighting those elements, with colour-by properties showing up synchronised through all widgets on the page at the same time.

Machine learning in design

CORE.AI is the specialist team within CORE studio, dedicated to Artificial Intelligence (AI) and Machine Learning (ML), with the objective to find new ways to apply these novel tools to our day-to-day engineering practice. Three equally important ingredients come to play when we think about automating and accelerating our structural engineering workflows with AI: Data, Machine Learning and MLOps.

The first ingredient on the journey is the Data feeding into any of our processes. Seven decades of engineering buildings and projects enable us to leverage and generate massive synthetic datasets exploring every corner of the design space. In a matter of days, CORE’s physics-based design engines can efficiently create millions of data points that would take decades to create by humans and even months by typical commercial software packages. Critical here is the curation of that data ensuring it can be contribute meaningfully to the next steps in our AI journey.

The next crucial step is applying the correct machine learning architecture and training strategies to the right question. CORE.AI’s data scientists have been training large deep learning models on massive datasets, tuning them to the specific design task at hand and applying them into design engineering apps. CORE’s journey in machine learning started several years ago with an app called Asterisk . This web-based tool predicts an entire buildings’ structural frame in a matter of seconds. The user input required is limited to the massing of the building, the size of the core, and whether it is an office, residential or commercial building. Asterisk would then utilise a trained ML dataset to generate the appropriate framing geometry and beam and column sizes for the entire building.

The app was a huge success and has been used many times during plenty of early-stage design options and was particularly well suited for super-tall tower design. Although showing early success, the design of real-life buildings required a more flexible approach, rather than the one-stop shop that Asterisk proved to be. Over time CORE studio started to pick apart this one large model into smaller and more specialised apps, delicately tuned to specific sub-tasks such as a single timber bay design, steel-braced frames, and the likes. These could then in turn be put to work in concert creating compositions of larger, more intelligent models.

Creating those larger workflows then automatically leads us to the third ingredient: Machine Learning Operations (MLOps) – or, how can we create an infrastructure for easily turning our AI experiments into lightweight microservices that can power other applications. For this reason, CORE studio has developed Cortex. Cortex

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

is a platform and infrastructure that allows the user, with only a few clicks, to turn their trained ML models into a powerful web API that can easily be consumed by other applications, whether that is a Python script, a web page, a Grasshopper definition, or a custom-made ShapeDiver app.

Cortex brings the power of machine learning experts and computational designers together allowing their skill set to complement one another. The ML expert can post their experiments on the platform, consisting of one or several ML models organised and trained with different versions of parameters, all within an intuitive user interface. When the ML expert is happy with a specific model and version, they can easily create an API endpoint with a specific schema

of inputs. This endpoint can then be shared and used in a design application put together by the computational designer.

Expanding the earlier example for the timber bay design; the Grasshopper definition computes the span and width of a standard floor bay spanning between columns, defines the loading conditions, and then calls the API endpoint which communicates with the trained ML model that lives on the Cortex platform. This API call returns a set of beam sizes for the main girders, secondary joists and slab thickness, which is then rendered to the correct size directly in the Rhino viewport. The app can subsequently be packaged and shared with every engineer in the firm, allowing them to quickly design a typical floor bay to a

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

high level of detail in a matter of seconds, satisfying code standards for both strength and serviceability, including floor vibrations.

The field of machine learning is moving fast. Generative ML is one of those fast moving and exciting fields. CORE. AI has been experimenting with models such as DALL-E 2 and Stable Diffusion creating an app that takes a simple text prompt and optionally a reference image as input, while generating 3d designs directly in Rhino. And furthermore, the team has been looking for this

app to utilise proprietary architects’ image libraries and therefore produce entirely new design options, based on a specifically trained style and materiality. Another experiment the team has been working on is to see how a pre-trained LLM such as GPT-4 and Natural Language Processing (NLP) can be utilised and further enhanced with information from all of Thornton Tomasetti’s previous projects’ metadata. This suddenly brings the entire firms’ knowledge to all engineers’ fingertips by just a simple text prompt.

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

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 years of experience of developing applications for data visualisation, manipulation and machine learning, CORE studio sees a huge potential for AI to enhance the AEC industry going forward into the future. All the while, ensuring that this integration is carried out properly, and executed and constantly reviewed by experienced AEC professionals.

Jeroen Janssen

@tt_corestudio

Jeroen is an Associate Director at Thornton Tomasetti and is leading the CORE studio team in London. CORE is Thornton Tomasetti’s Research and Development 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

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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

SageGlass Classic offers four tints: clear, light, medium and full. When the tinted states are activated, the entire pane is tinted to control luminosity. In addition to these four tints, Harmony has no less than four additional tints (eight in total). Harmony is the latest solution invented by SageGlass’ team. This revolutionary Smart Window is the only product capable of tinting on a gradient, delivering targeted glare control and maximizing daylight with a more natural aesthetic.

When tinted, SageGlass products can block up to 96% of solar heat, helping to optimize energy efficiency and thermal comfort in the building. It also allows 60% of daylight to pass through in its clearest state, and blocks up to 99% of daylight in its darkest state, optimizing light

levels without visual discomfort while preserving the view to the outside.

But how does SageGlass work?

First of all, it is important to know that SageGlass electrochromic glass works thanks to three components: the insulating glass, the control material and the intelligence of the system.

- The insulating glass unit (IGU) consists of five layers of ceramic material that are coated onto a thin piece of glass. Applying a small electrical charge causes lithium ions to transfer layers making the glass tint. Reversing the polarity causes the glass to clear.

- The second component is Controls Hardware. This is the communication hub. It has interfaces for customer

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Fig. 2: System overview , © SageGlass

interactions and then transmits information across the building.

- The final component, the System Intelligence, takes in the outdoor and indoor factors such as the weather, the sun’s position, building orientation, location, occupancy, and time of day to create optimal comfort for every occupant.

Thus, to perform in the field, electrochromic glass needs software and controls to dictate when and how it should tint. Using a combination of predictive and real-time inputs, the software and controls manage daylighting, glare, energy use and color rendition throughout the day. At any point you can control the glass, manually adjusting the settings to your desired tinting preference from a wall

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Fig. 3: The glass tinting process , © SageGlass

touch panel, mobile app or BMS (Building Management System).

SageGlass is therefore far from being a traditional glass. The attraction for SageGlass is not by chance. Indeed, there are many advantages in the installation of this Smart Window, which, let's not forget, tints automatically. Its primary function is to control the luminosity and heat entering the room. Traditional buildings use manual blinds, which are typically down 50%-70% of the day, blocking daylight and views. Smart windows solve that problem. They allow natural light to flow in the building and thus considerably improve the comfort and well-being of the occupants who are no longer bothered by glare or heat.

Our latest study at Brownsville South Padre International Airport shows that when SageGlass is on, occupants are 3 times more satisfied with thermal comfort, 2.5 times more satisfied with light levels and 2.5 times more satisfied with glare control.

Electrochromic glass can significantly improve energy efficiency and help you achieve green building certifications for buildings. Indeed, smart glass responds to changes in the environment, limiting the heat or cold entering the space, and can reduce loads on HVAC systems. It saves an average of 20% of energy.In addition, thanks to dynamic glass, the occupants of a building continue to have a direct view of the outdoors, which

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Fig. 4: SageGlass at Schwarzsee Hostellerie in Schwarzsee. ©SageGlass

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. Architects can design buildings without worrying about the installation of blinds or shades: they can therefore design buildings that are fully glazed, comfortable and beneficial to the health of the occupants.

To date, SageGlass Smart Windows has been installed in many buildings around the world. These include Nestlé's headquarters in Vevey (Switzerland), Schneider Electric's new building called IntenCity and located in Grenoble (France), and Powerhouse Telemark in Porsgrunn (Norway). SageGlass is not just for offices. It can also be installed in buildings in other industries: for example, in airports (Nashville airport in the United States), in buildings in the hospitality sector (Marriott Hotel in Geneva, Schwarzsee Hostellerie in Schwarzsee), or in educational buildings (Ruselokka Skole in Sweden), and many others.

Sageglass continues to refine its dynamic glass: the teams are trying to improve the comfort and aesthetics of the glass even further.

@sage_glass

Raimond Starmans is the Country Director for Germany and The Netherlands for Saint-Gobain's SageGlass since the end of 2019. Based in the Euregio in the Netherlands, Raimond is a big promotor of sustainable dynamic glass solutions together with his sales and technical support team. He has more than 30 years of work experience in the architectural glass industry, collaborating with architects, consultants, main contractors, applicators and fabricators in major projects

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Raimond Starmans

Architects of Conditions SUPERWORLD

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

Supported spatial mediation of the roofscape

Above Rotterdam, a landscape of rooftops slowly appears in our common consciousness. Some of these rooftops are already today being used for various purposes such as creating additional living spaces, setting up communal food gardens, and setting up temporary cafes. Such activities prove the vast potential for systematically intensifying urban areas, addressing some of the city's broader development needs such as promoting biodiversity, ensuring affordable housing, generating renewable energy, and mitigating flood risks.

Roofs are private spaces evolving in a tight relationship with its public environment. The question is how to interweave the very individual spatial agenda of each roof and its owner with the development of sustainable futures for the collective. RoofScape aims to build that bridge. It aggregates individual contributions to shift from the few large to the many small urban interventions planning, and what it means to support it by policy making.

The project proposes a data-driven method that, on one side evaluates the existing capacity of roofs, and on the

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

other side simulates scenarios of desired alternative futures. In its prototype stage, RoofScape is the first selfinitiated collaboration between Superworld, MVRDV Next, and the Municipality of Rotterdam, a prototype of how to co-create experimental tools and push the boundaries of the current protocols in city-making.

RoofScape’s capacity analysis is carried out via a computational model that systematically evaluates individual buildings and compares their properties with the requirements of different rooftop programs. It

answers questions like: How high is a roof? How much solar radiation does it receive? Is it in proximity to an urban green corridor? This analysis is performed on the scale of the entire city, allowing users to explore both a macro level and the micro level of individual rooftops, through multi-layered open-source data and linked to the digital twin of Rotterdam. The result of it determines how well distinct functions, such as green roofs, solar panels, public program or housing can be allocated onto a single roof.

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

Rather than coming up with definitive proposal, or a masterplan of the entire rooftop landscape, RoofScape envisions a playful way to generate scenarios through 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 targets for individual programs. Algorithms value trade between the capacities of the rooftops and the desires of the user, creating a variety of plausible rooftop scenarios. Each scenario is visualized in an interactive 3D city model, which allows for qualitative evaluation by the user in addition to quantitative evaluation such as housing units, kWh of energy production, or m3 of water retention that is automatically provided by the tool.

If we truly aim to see private roof spaces participating to collective climate transitions, one needs to critically integrate all the different stakeholders that make up the ownership fabric of a city. When we look at the wide range of ownership type in the city, ranging from individuals to housing cooperatives, associations, corporate and public buildings, a comprehensive strategy for rooftop activation needs to engage stakeholders more than anything else. It needs to present potential roofscape developers with an idea of spatial qualities as well as the environmental and economic impacts of these activations.

The question then becomes not only one of data, but instead one about infrastructure. It is about crafting the conditions for institutional and citizens initiatives to not only co-exist but complement and amplify each other. It is about providing the capacities for each to become gardeners of building processes. Fundamentally, it is about creating tools for a participatory infrastructure,

in which citizens are at the heart of a process. And digitalization will play a crucial role in making this next step.

Augmented citizens negotiations of retrofit

The energy transition of the already existing built environment is more than solely a technical issue; it is also a social challenge. It means coping with existing ownership situations, intrusive retrofit measures, slow decision-making processes and uneven value distribution of eventual retrofits. Large scale retrofitting activities are urgently needed to reach our climate targets but the decision-making process in buildings with shared ownership models is challenging. In the Netherlands, over 1.5 million homes are part of associations of coowners, called VvEs (Vereniging van Eigenaars), making the decision-making process towards more sustainable buildings increasingly complex, but highly relevant.

Negotiating Energy is a research project prototyping new methods for a more inclusive energy retrofitting process within home co-owner associations. The project is putting in place the scaffolding for a scalable model of simulation-based retrofit measures through Augmented Negotiations. It combines a digital energy modelling system to support the human and intrinsically social experience of collective decision making.

The project develops this approach within the real conditions of two communities. The collective governance model of a self-organized living group, where ongoing decision-making about energy as a resource is common practice already. The model is then tested in a more

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Fig. 4: Augumented collective negotiations. ©Superworld Fig. 5: Negotiating energy retrofit possibilities - Facade and windows ©Superworld

regular association of co-owners in the historic center of Amsterdam, exploring how negotiations of retrofit measures can be driven collectively. The selected cases we focused on monumental, protected buildings. These are notoriously the most difficult to retrofit – adding another layer of complexity for homeowners who need to deal with extra constraints and approval from monumental comity – and therefore the most challenging to see clear paths of upscaling.

Through interviews and workshops, the researchers include the community’s preferences into a parametric modelling process to guide them in their decision. Parametric design and energy modeling tools like grasshopper and ladybug were used to create the multicriteria model and iterate all possible solutions, not as simple data assessment. The goal is to define a catalogue of realistic and best-performing collective energy measures (post-insulation, architectural interventions), which the group can decide and negotiate upon. In the playful form of a physical card game, complex digital modelling is translated into an inclusive medium, 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 their boiler, based on information like costs, carbon savings, energy bill reduction for each measure.

The energy transition is about defining new protocols in which societal missions are pursued and facilitated by better system design. At the intersection between strategic design, building technology and citizen science, the exploratory research Negotiating Energy aims to design a replicable and collective retrofit approach by reframing

the cultural notion of energy. It brings together the architects from Superworld with knowledge institutions TU Delft and AMS Institute and combines data-driven and social science approaches to solve the decision bottleneck faced by VvEs regarding energy retrofitting.

The project amplifies the conventional negotiation procedure by integrating a data-driven assessment of possible retrofitting measures and a collectively accessible negotiation medium for non-experts into the process. It explores on how to translate energy modelling into a enhanced social experience for constructive collective negotiation and decision-making.

Deepening citizen's understanding of their retrofitting possibilities supported by institutional and local knowledge aims to increase capacities within owner associations for more impactful energy refurbishments. Ultimately, the project seeks to facilitate the energy transition in the built environment by proposing an intrinsically social, yet data-driven framework for truly collective energy retrofitting.

Designing conditions

These two projects are some of Superworld’s recent probes on how, using data and tooling, we can create the conditions for sustainable futures. Both explore the design of the object and the medium, the support of conversations and negotiations, whether it is about space or resources. They emphasize the importance of imagining not only the inspiring tangible futures, but also the necessary organizational and social infrastructures to support that vision.

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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.

@build.superworld

Superworld is an international practice operating at the interfaces of architecture, technology 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.

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Thomas Krall Maxime Cunin

AiDAPT LAB

AI lab for design, analysis and optimization in Architecture and Built Environment

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.

Dr. Charalampos Andriotis, Dr. Seyran Khademi Fig. 1: Exploiting topology © Casper van Engelenburg

At AiDAPT, computer vision, data science, and decision optimization methods come together through the development of deep learning, reinforcement learning, and uncertainty quantification frameworks that help us analyze and synthesize decisions for architectural and structural systems. This entails operation and reasoning in the complex spaces created by the confluence of diverse imagery data, noisy sensory measurements, virtual structural simulators, and uncertain numerical models. Application themes of interest range from automatic recognition of architectural drawings in large databases and generative design recommendations (initial design phase) to predictive intervention planning for life extension, structural risk mitigation, and multi-agent optimization of built systems (life-cycle optimization phase).

Bridging fundamental and applied AI, AiDAPT aims at creating new scientific paradigms towards a more reliable and sustainable built environment.

The AiDAPT Lab is part of the TU Delft AI Labs programme belonging to the university wide initiative of AI Labs. The 24 TU Delft AI Labs are committed to education, research and innovation in the field of AI, data and digitization. These labs are an embodiment of the bridge between research 'in' and 'with' AI, data and digitization. They promote cross-fertilization between talent and expertise and work to increase the impact of AI in the fields of natural sciences, design techniques and society.

AiDAPT Lab is a BK lab connecting two departments, AE+T & Architecture, maintaining also strong connections in research and education with other departments and faculties, such as the Computer Science Department.

AiDAPT is co-directed by Seyran Khademi (Architecture) and Charalampos Andriotis (AE+T), currently involving 4 core PhD students, who work on some of the grand challenges of the built environment, at the interface of architectural design and structural engineering. Directors (Dr. Charalampos Andriotis, Dr. Seyran Khademi)

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Fig. 2: AiDAPT logo. © AiDAPT

Deep Image Search of Architectural Drawings

Casper's research is about the development of selflearning algorithms that can effectively understand visual as well as compositional patterns from architectural image data, ultimately to equip software to become a more competent partner in design. He develops deep learning frameworks that enable us to learn low-dimensional, task-agnostic representations of architectural drawings, specifically focusing on deep image search, i.e., image retrieval algorithms. This research line builds a foundation for large quantitative analysis of archival and linked visual data. Besides theoretical work, his aim is to connect it to the practice by enhancing Architectural-specific search engines.

Visible Context in Architectural Environmental Design for AI-driven Building Design

To decarbonize by 2050, as agreed in the Paris Climate Accord, immediate action for lowering the environmental impact of the building sector is needed. Fatemeh’s research, titled “Visible Context in Architectural

Environmental Design”, aims to incorporate computer vision to help designers navigate through environmental building design. Accordingly, the following projects are framed to be conducted along her Ph.D. trajectory:

(1) Building context visualization pipeline based on the big data on the environmental simulation results (e.g. daylight, view to outdoors, and noise), (2) Environmental spatial zoning design using computer vision, (2) Intelligent environmental design tool development to be served as building performance analysts’ assistant.

Structural Integrity Management of Large Scale Built Environment Networks

Structural systems must satisfy multiple performance and functionality requirements during their life cycle. However, they are subjected to different degradation mechanisms due to time-dependent stressors and hazards, affecting the aforementioned requirements and preventing the structural systems from functioning properly. In view of an aging and growing built environment, intervention and data-collection strategies need to be planned efficiently

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Fig. 3: Compositional thinking. © Casper van Engelenburg

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 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.

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Dr. Seyran Khademi Dr. Charalampos Andriotis Casper van Engelenburg Prateek Bhustali Fatemeh Mostafavi Ziead Metwally

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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

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 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.

Being born and raised in Manaus allowed me to follow the transformation and impacts that the city went through during the construction of this housing complex. This subject motivated me to think and debate ways to allow inhabitants to custom design their homes so their social and cultural patterns of using space can prevail while keeping the construction cost down. Another question raised was how to facilitate the discussion and participation of multiple actors in the design process while keeping the design process simple without the obstacle of technical knowledge and still ensuring a certain level of quality. Although I used my hometown as a study case, similar situations exist in other countries, and it would be relevant to create a game that can adapt to any context. Therefore, I developed a meta-game framework,

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

Research Framework

The post-occupancy renovation process mentioned earlier highlights a couple of gaps in the mass housing production, like lack of fulfilment of user needs, minimum or no user participation during the design process, and the need for renovation of the housing unit to adapt it to the user’s preference and need. Through the literature research, I could match adequate methods and concepts to solve those problems. For example, a participatory design method would allow multiple actors in the design process, and a serious game method can engage and empower people to express their requirements. While mass customization, modularity and generative design enable replicability and affordability to be achieved together with meeting the individual needs of diverse users.

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

Other methods and concepts used to study the context and to facilitate the applicability of the game were (a) Space Syntax that exams the space independently of its forms, shape, dimension, orientation, or location, instead concentrating on the topology of the space, which reflects and embody a social pattern, incorporating collective values and the social structure of a society, (b) Cultural Values, focusing specially in Social Values which make people create a memory of the space and feel attached to it, consequently this brings a sense of identity and belonging to a place, and (c) Open building, a bottom-up design approach which works with the introduction of several levels of decision-making in the building process, and the possibility of decoupling building parts with different life cycles.

The literature research helped in developing design principles for the game. And the result was a construction game that is a mix of a board game with Lego pieces. Each Game Piece is compatible to Lego and has different dimension, giving freedom to players to choose the possible function that can represent, while the gameboard represents the plot on which the housing is implemented. Additionally to this, cards are used as play mechanism to engage the players. The game is divided into five stages which will order the design decision-making process from abstract to concrete.

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Fig. 3: Game Piece variation.

Game stages

Planning is the first stage where the end users will express and specify their criteria, preferences, program of requirement, spatial organization, and social logic of the space. This stage aims to reach spatial specification, spatial relation, and collective design goals. One of the outcomes will be a draft configuration made of Game Pieces that they are going to use as a guide for the next stage of the game.

The Zoning phase involves game mechanisms, which role is to engage players, motivate them to work together, and induce them to discuss and make commitments. Activity Cards used during this stage of the game are adapted to the context, since they translate the possible needs and preferences of the user, based on Context Information from the previous stage.

Routing allows the end user to customize the interior of each space module through a modular system of furniture, allowing the visualization of hidden corridors and understanding of the walking flow from one space to

another. As part of this procedure, the end-user must specify where the openings will be placed and which type. The shape grammar and modular furniture can vary according to the Context.

The Evaluation phase checks the spatial quality and validity of the user-generated design. This stage aims to inform if a design is valid or not. In case of being invalid, point out a recommendation to improve the design and return to the Zoning Stage. The criteria used to evaluate the design should be adjusted to the reality in which is being applied, therefore it should follow the Building Code.

Shaping is the last stage, it concerns the materialization of the building geometry, determining the aesthetics of the design and concretization of the building. Different options will be available so the players can select the type, size, design and position of walls and openings from the wall modules. This stage must be crafted to the local architecture and style of where the building is being inserted.

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Fig. 4: Example translation Game Pieces to floorplan.

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

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Vasilka Espinosa @vasilkaespinosa

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

DEEP - LEARNINGBASED STRUCTURAL BRIDGE DESIGN

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

Sophia Kuhn, Dr. Michael Kraus, Prof. Dr. Walter Kaufmann

As our society faces increasing demand for sustainable infrastructure, the need for efficient and effective design tools is greater than ever. One promising approach is the use of artificial intelligence (AI) in the conceptual design stage of a construction project to allow for informed decisions based on data. Our research project focuses on using AI for the design, analysis and optimisation of concrete bridge structures.

The conceptual design stage of projects in the architecture, engineering and construction (AEC) industry commonly lacks performance evaluations but relies predominantly on the structural designer’s expertise. However, particularly bridge design projects involve a great complexity due to the tremendous amount of interdependent design parameters, multiple often conflicting objectives, and many involved stakeholders. Consequently, the current design practice leads to a time-consuming, iterative design process, often requiring

expensive design alterations in later project phases. Emerging generative design methods facilitate design space exploration based on quantifiable performance metrics, yet remain time-consuming and computationally expensive. On the other hand, pure optimisation methods ignore non-quantifiable aspects (e.g. aesthetics or constructability). Recent breakthroughs in AI have shown their great potential to improve this situation by transforming many industry and research fields. However, adoption to the AEC has hardly been done, mainly due to the lack of systematised, freely accessible data and the need for incorporation of in-depth domain knowledge. At its core, our approach is based on our developed variant of conditional variational autoencoders (cVAE) (see Figure 2). We generate synthetic data using parametric modelling and state-of-the-art simulation software, which we then use to train the cVAE. The advantage of the cVAE over traditional simulation software is that it can speed up the performance prediction based on a

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Fig. 2: Conditional Variational Autoencoder (cVAE) architecture implemented within the toolbox (Balmer, Kuhn et al, 2022). x: Design feature vector, y = P(x): Performance vector,ŷ: Predicted performance vector, x: Generated design feature vector conditioned on a chosen performance vector y, z: latent representation.

set of design parameters (Forward Design) while also being able to generate new designs conditioned on a set of performance objectives (Inverse Design) (see Figure 2). The latter remains impossible with state-of-theart simulation software, such as finite element analysis software.

From a dataset of synthetic bridge structures, we extract design features x such as dimensions, materials and loads and performances P such as load-bearing capacity, environmental impact and cost. We then use the features as inputs to the cVAE, which at the same time learns to (i) generate a latent representation z, (ii) predict the performance ŷ, and (iii) reconstruct x . Once we have trained the cVAE on synthetic data, we can use it to generate new designs. To do this, we start with a set of performance objectives y, such as a desired load-bearing capacity or a target cost. We then condition the cVAE on these objectives and use it to generate a set of design parameters x that satisfy these objectives.

A first prototype was developed for the pedestrian bridge

“Brücke über den Graben” in St. Gallen (Switzerland) planned by Basler & Hoffmann AG (Balmer, Kuhn et al, 2022). Using Dynamo within Revit and SOFiSTiK a data set was generated for the real-life project situation at hand. It was shown that both the forward and inverse design mappings can be learned accurately by the cVAE implementation and the evaluation time is substantially reduced, after an initial data generation and training phase. Additionally, the deep neural network model also provides partial derivatives which gives an understanding of the parameter and performance sensitivities. The prototype interface within Dynamo can be seen in the

Researchers from kfm research and SDSC are developing a toolbox which implements the introduced approach to be used within the structural design stage of construction projects today. The software toolbox is developed structure agnostic and can therefore be applied to any structure type with parametric representation. The implemented software modules are shown in the system map in Figure 3. Additional to the synthetically generated data set, the framework is further informed by a second data base of existing bridge structures. Prior models are trained to extract utilised design patterns form existing bridge structures to inform and validate the Forward and Inverse Design Models (Kuhn et al, 2022).

Applied to a structural design project, the framework in development allows to rapidly explore the design space and identify optimal designs that meet the given performance objectives. It provides an understanding of the dependencies of parameter and performances of the high-dimensional design spaces and allows for the derivation of design rules for the analysed structure types. It can therefore boost key decisions of the conceptual design stage of an AEC project and foster efficient, economic and sustainable bridge structures.

However, the use of AI in bridge design has limitations. The generated designs must still be evaluated and refined by a structural engineer to ensure their safety and feasibility. Therefore, a focus within the framework development is the interactivity between the AI and the structural designer. While the AI augments the analytical and creative capabilities of the engineer, the structural

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Figure 1 (previous page).

engineer should be able to steer the design process, bringing in additional information about the individual project context and non-quantifiable or even subjective performance goals (e.g. constructability and aesthetics). In conclusion, the use of AI in the conceptual design stage of a bridge design project has the potential to greatly enhance the efficiency and effectiveness of the design process. Our research project primarily focuses on using cVAE architectures as meta models for analysing and optimising concrete bridge structures. However, we furthermore apply the toolbox in the scope of research 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.

References:

(Balmer, uhn et al, 2022): V. M. Balmer, S. V. Kuhn, R. Bischof, L. Salamanca,. K aufmann, F. Perez-Cruz, and M. A. Kraus, “Design space exploration and explanation via conditional variational autoencoders in meta- model based conceptual design of pedestrian bridges,” 2022 .

(K uhn et al, 2022): S. V. uhn, R. Bischof, G. Klonaris, W. Kaufmann, and M. A. raus, “ntab0: Design priors for AIaugmented generative design of network tied-arch-bridges,” Forum Bauinformatik 2022.

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

Sophia is a doctoral researcher at the Chair of Concrete Structures and Bridge Design at ETH Zurich. Her research focuses on the intersection of structural engineering and artificial intelligence. Within Design

++ projects together with the Swiss Data Science Centre (SDSC) she currently works on the development of a domain aware AI Co-Pilot for the conceptual design of concrete bridge structures. She is teaching assistant for classes on Scientific Machine Learning and Structural Concrete. Sophia Kuhn studied civil engineering at the ETH Zurich and University of California Berkeley.

Dr. Kraus is a Senior Researcher at the Chair of Concrete Structures and Bridge Design and Co-leader of the Immersive Design Lab of the Design++ Initiative at ETH Zurich. He researches and teaches in Scientific Machine Learning for the built environment. His work includes the development of theory-informed machine and deep learning methods and their application with a particular focus on structural design, component verification and generative design. In order to ensure the transfer of knowledge into practice, he runs companies with a focus on industry-related applications of artificial intelligence in addition to his university activities.

Prof. Dr. Kaufmann is the Chair of Structural Engineering (Concrete Structures and Bridge Design) at the ETH Zurich. His research focuses on the loaddeformation behaviour of structural concrete, structural safety evaluation of existing bridges and buildings, as well as innovative interdisciplinary research projects. He additionally acts as board member and expert consultant for the engineering firm dsp Ingenieure & Planer AG. He has been successfully involved in several bridge design competitions, structural engineering projects, and expert appraisals, particularly regarding the structural safety evaluation and retrofit of existing bridges.

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Prof. Dr. Walter Kaufmann @ethzurich Dr. Michael Kraus @ethzurich Sophia Kuhn @ethzurich

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.

Taking the right turn

Our process, called “The Augmented Architect,” involves using digital processes and workflows based on AI and automation to connect data-driven tools with the architectural design process. We have established LINK IO, a computational network connecting architects, computational designers, and data specialists to develop a toolbox for augmented analysis. Our approach enables us to evaluate our designs throughout the entire process and create architecture with impact.

Developing Building Sketcher 2.0: From Grasshopper prototyping to software development

Most craftsmen rely on specialized tools for their exact tasks. In LINK Arkitektur we have embraced that designers work in different digital tools in different locations and roles. Our Norwegian colleagues make their detailed design in Archicad whereas in Denmark it happens in Revit. Our landscape architects use Autocad with a sprinkle of Sketchup. Most of our early phase planning, volume studies and sketching are conducted in Rhino. Because of the robustness, fair license costs and the well documented API to extend the functionality, we decided to develop a framework that would give the architects superpowers in Rhino3d while maintaining a good interconnection to all of the other tools.

Over the years we have used various simulation tools ranging from Energy Simulations in EnergyPlus, Daylight Simulations in Radiance, Climatic studies in Ladybug and more. Creating automated calculations from various disciplines inside of the visual programming tool of Grasshopper allowed us to setup prototypes and apply our specialist knowledge on projects. While this has

worked well for our specialists, we wanted to democratize these tools in a holistic framework to make it easier for the designers in LINK Arkitektur.

Rewriting everything from scratch with C# code in a framework allowed us to make things more fluid, faster, user-friendly, and long term maintainable. We call the framework LINK_EP, short for Early Phase. Introducing a few simple data classes of a Building, Room, Windows, Balconies and a few other widely used objects in BIM modelling while still having the agility of “just sketching”. This ended up being implemented into plugins in Rhino and Grasshopper which allow us to make general analyses for energy and performance but also more bespoke analyses for different national requirements in Norway, Sweden, and Denmark. You can hear more about LINK_EP at the McNeel webinar, using link number 1 in the footnotes.

The implementation, which we call Building Sketcher 2.0, presents a user interface which allows architects to access essential microclimate simulations, including Sunlight Hours, Shadows, MOA (minimum outdoor area), Pedestrian Comfort, and sustainability analysis like early phase LCA Guesstimating, Daylight with the Vertical Sky Component or the 45° / 10% rule (Scandinavian standards) and Radiation. Additionally, an Area Analytics module is integrated to enhance early phase massing and planning. This is, of course, customized to national area requirements. By writing this in a modular code base, additional modules can easily be added.

We had a few projects that needed bespoke tools such as a view analysis and a safety analysis. After completing and evaluating these projects, the code was generalized

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and integrated into the LINK_EP framework. Currently our development is leading us towards interoperability through industry standards such as IFC, Speckle, JSON, and even automated PDF reports. This will be developed in parallel with demands on the projects. With a good foundation, it only requires the extra 20% to generalize a workflow so it’s useful for others.

As part of this development, we also created the LINK_ Dashboards plugin which can visualize data and geometry directly in Rhino. This part is now shared in public and is downloadable for free, using link number 2 in the footnotes.

As a parallel line of our development, a workflow to perform analyze Outdoor Comfort through Pedestrian Wind Comfort (PWC) analyses using CFD calculations is also part of our toolset. This type of analysis is especially relevant for our projects and clients within cold and windy climates in Scandinavia. You can see more info of this line of development using link number 3 in the footnotes.

Think of it like your smart watch: By exposing how active you are, it nudges you into taking an extra walk. In the same way we visualize project performances and nudge architects to take knowledge informed decisions. This

enables architects and designers to evaluate the design of a building throughout the early stages of the project, allowing for the assessment of a multitude of complex environmental and performative factors. In short, the Building Sketcher 2.0 represents a critical tool in the contemporary architectural design process, offering architects and designers an innovative approach to the planning and design of the built environment.

Adaptive reuse enhanced through augmentation

To apply the philosophy of the augmented architect to the early phase design process of reuse projects, our industrial PhD student, Povl Filip Sonne-Frederiksen, is developing tools that automate segmentation of building point cloud scans in to solid object 3D models. By obtaining object-based models, stakeholders can make informed decisions about the design and reuse of existing buildings, ultimately exposing potential and reducing risk which can lead to less demolition and more building transformation.

Despite the advancements in point cloud segmentation in other fields, challenges still exist, particularly in the AEC industry. Moreover, the large size of point cloud scans in typical buildings can pose challenges in terms of processing time and computational resources. Therefor

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

one of the techniques that we are looking into consists of using RGB-D images, which are regular images with an additional map describing the distance to the camera.

Using these images, a machine learning model is trained to recognize and distinguish between different objects and elements within a scene. Those are turned in to point clouds using the depth component of the image which then can then be stitched together into a larger scan representing the whole building. The following process then isolates individual elements and simplifies the geometry into solid 3d objects.

This workflow will essentially simplify early phase mapping of buildings and speed up common tasks such as making an inventory, the detection and deconstruction of building elements as well as planning material reuse. It also lays the foundation for the study of different scenarios as well as defining a base model for stakeholders to collaborate around.

In summary, leveraging point cloud segmentation to obtain 3D models of existing buildings can enhance the design process and increase the rate of building reuse, in line with the philosophy of the augmented architect. Challenges still exist, but we at LINK are excited for the possibilities that will come.

Automatic floor plan generation

While building on our toolkit we always try to partner up with the fastest movers in the industry and new AI tools have been booming. One of the most prominent areas of development is plan generation, there are several promising AI initiatives, such as Finch, PlanFinder,

Architext and others. These first actors are moving fast and are producing rapid and impressive results, but as always, we should ask ourselves what the use cases might be for this technology.

Plan drawings is something that architects have a ton of domain knowledge within and can draw and plan out these at a very fast pace. Is plan generation really such a crucial part to automate and what do we see could be the future use cases that build upon this. And is the quality really on par with a professional now? And what about next year?

One of the biggest challenges for AI-generated plans is the lack of localized datasets. However, the use of graph mapping which for example Finch is working with can address this issue but creates a need for complex graphs and rulesets. While the latest AI technology still requires some manual corrections, the potential is huge. As we see it, they are particularly useful for analysis purposes since they have all the necessary relations and oftentimes are tagged and ready for machine use.

As we develop workflows, we see that having a tagged and ready to use floorplan is a major advantage for early phase analysis. Use cases that we have applied this too is for LCA calculations where the layout of interior elements is less important the amount of each. Daylight is another analysis that gains largely from this, while the layout is important for daylight calculations interior room dimensions can be seen as a larger factor.

By leveraging the best parts of generative neural networks, we can provide more precise analysis on very

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simple geometry which can be quickly filled in to give a more comprehensive picture of our projects. By using the current state of the art technology and keeping our tools adaptive to when the technological breakthrough happens which it will, we can futureproof what we build.

The Augmented Architect anno 2025

When asked to predict the future in the field of architecture, it’s surprising how close the “far future” is now. Prominent AI researchers in 2021 found it impossible for computers to understand jokes and understand intentions in written

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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.

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. app generates your slideshows, and Microsoft Teams automatically creates to-do lists and suggests follow ups after meetings. The most terrific and terrifying part is that most of the above can be done on a smartphone from your sofa with no prior domain knowledge.

For us, a bunch of architectural techies who really enjoy diving into the technical aspects of machine learning, the most annoying part is that the time we invested in learning something last year is already deprecated. Someone has already made an app for that in the meanwhile.

So, think about it. As our everyday becomes smarter and smarter with the help of AI, so does the everyday of the people developing that AI. Now the AI capability is growing exponentially, and our industry has only scratched the surface. All we can do is embrace the new powers we get and remember why we choose the architectural profession: To create better conditions for people and planet.

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

LINK Arkitektur is a Scandinavian based architectural office with around 500 employees. We specialize in large urban developments, residential architecture, and hospitals. Currently around 10 employees around all of Scandinavia are attached half time or full time to our computational network, LINK IO.

You can read more about LINK IO at www.io.linkarkitektur. com

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

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Mathias Sønderskov Schaltz, Jan Buthke, Povl Filip Sonne-Frederiksen, Fabian Sellberg @ LINK Arkitektur

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?

MACHINE LEARNING FOR THE BUILT ENVIRONMENT

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.

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

The automation of workflows

The idea of automating creative workflows in much the same way we have other types of work is not exactly new. The first Generative Adversarial Network (GAN), for example, dates as far back as 2014. GANs are particularly dear to designers because they are capable of generating images, starting from a text, image or even tabular prompt. The popular DALL-E and Midjourney are an example of this class of models, though they are specifically designed for unrestricted creativity (which sometimes strays quite far away from the text prompt). GANs can also be developed to follow stricter rules, such as being bound to a specific region of space or to a specific colour palette, to address design problems. One remarkable example of this approach is the excellent work of Stanislas Chaillou and his ArchiGAN model of 2019[4], capable of designing

an apartment floor plan to fit into any given apartment shape, provided only with the location of the windows and entrance.

Deep learning - CNN and GAN

Convolutional Neural Networks (CNNs) are also extensively used in the analysis of the built environment for their ability to extract features from a single image. From identifying solar panels from the satellite images of roofs (such as in Roberto Castello’s study of 2019[1]) to the detection of built environment health effects provided by Xiaohe Yue in 2022[2], there is no doubt that CNNs can play a decisive role in the development of future living spaces that are both functional and aesthetically pleasing.

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

The basic idea behind GANs and CNNs is very simple: images are matrices of pixels, and the position of each pixel tells us something about the nature of the object represented in the image. By passing a small filter over this matrix, we can extract numerical values which mean nothing to human beings but can be used by the algorithm to determine the relationship between each group of pixels. In the case of a CNN, these values are used to calculate the probability that the image belongs to one of the predefined categories - for example, “cat” or “not cat”. In a GAN, they are used to repeatedly generate new pictures, which are then compared to the ground truth by a classifier. If the imitation is good enough, the training is complete; otherwise, a new cycle is run with slightly different parameters.

3D-based Machine Learning

There have, in recent years, been significant steps forward in our understanding of the applications of 3D based 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

clouds are one of the most helpful technologies when 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 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.

Other significant factors for Machine Learning in the built environment

Convolutional and 3D models, however, are not the only tool at our disposal. There is more to architecture than images. Especially when developing large real estate projects, one must take into account numerous other factors, such as financial feasibility, environmental impact and sustainability.

At OMRT, we are experimenting with predicting these factors through the power of Machine Learning. One of our experimental prototypes, Ocelot, predicts the price of real estate by category and location for up to 5 years into the future with a 10% margin of error. Stakeholders in most of our projects have an interest in knowing this as it allows them to anticipate costs and profits early in the development process. We are currently also working on a new model to synthesise Daylight data (very timeconsuming to calculate) from Skyview results (which are quick and easy), Horus. These models rely on more “traditional” techniques, such as gradient boosting and random forest regression.

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Fig. 3: An example of a GAN model architecture

Limitations

Sounds great, right? Now let me tell you about something ML can’t do. A large chunk of our resources is dedicated to computational fluid dynamics (CFD). In a typical CFD study, we receive some 3D models from our clients and we observe the effect of simulated wind flow from 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

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 chain. Thanks to tools like Ocelot, our real estate unit price prediction model, we will soon be able to provide meaningful insights into the future to our clients, which was something we could only do in broad terms before. And Horus, by converting Skyview analysis to Daylight predictions in a matter of seconds, will significantly cut computational costs and time for our engineers, who will have more time to dedicate themselves to tasks in which

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Fig. 4: Typical Ocelot output, highlighting areas in which detached houses will be more expensive (green) or cheaper (red) © OMRT Fig. 5: Typical CFD output. Note the red area is where wind speed is higher. © OMRT

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 OMRT has so far been and will continue to be decisive in the years to come, when we will automate more and more of our processes to give our and our clients’ engineers the time and insight they need to deliver astounding, groundbreaking results.

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

[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/

At OMRT we are enthusiastic about using computational tools to make designers' lives easier. Our company is a fast growing startup founded by two TU Delft alumni in 2018 after being disappointed by the inefficiency of the tools used in the AEC project development. We help our clients get more insight into their projects using computational analysis and integrate tailor-made digital tools into their workflow.

Raffaele is a Data Scientist and Lead Researcher at OMRT. He enjoys using Machine Learning to make his own and other people’s lives easier. He graduated with a BSc in Economics and Finance from the University of Amsterdam, followed by a MSc in Economics from Warwick University.

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about OMRT @OMRT Raffaele Di Carlo @OMRT

INTEGRATED BIOINSPIRED DESIGN BY AI

Using cell structure patterns to train an AI model to explore topology design ideas

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

Fig. 1: VAE Architecture © Namrata Baruah Namrata

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.

This thesis explores the possibilities of an AI–design integration. Still, for the methodology to be practically incorporated into the design process, further development of the AI model is required as well as exploring other advanced generative AI models would be beneficial.

Current Scenario and thesis Workflow

Research shows that significant design decisions such as building geometry, structure, massing, etc. - which account for more than 75% of the final product costs - are taken during the conceptual design phase. The tools must become more versatile and allow for exploration to aid the designers during the conceptual design phase. AI can provide a solution. Generative AI can detect the underlying pattern related to the input dataset and produce similar content.

The thesis focused on topology exploration as it only

Fig. 2: Thesis hypothesis © Namrata Baruah requires an initial domain and can be used to explore and validate ideas during the early design stages. The literature available for topology mainly focused on optimization rather than exploration. However, one study (Sigrid Adriaenssens, 2014) found that while optimizing a circular shell structure, the optimized topology resembled a diatom found in nature. Nature shows us optimized geometries for lightweight structures, and cellular solids make up many materials in nature.

For the scope of the thesis, the patterns of cellular solids were used as input data to train a generative AI model and use it to generate new data, which can add to increasing the design space for the exploration of options.

The thesis had four main objectives to provide a workflow that can be used to explore the application of AI as a tool for generating design ideas:

1. Selection of cellular patterns to train the model.

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2. Generating a dataset from the selected patterns

3. Training the VAE model

4. Creating a workflow for integrating the VAE results into the design process. The research framework is shown in Fig 3.

Dataset generation

AI needs a lot of data to train. There can be many sources for training data. But, if the goal of the training is something specific and custom. It is better to create an artificial dataset.

For the thesis, the input of the dataset would be 2D images. For that, I looked at lattice structures, which are a sub-class of cellular solids; they are also present in 2D, meaning that all the structural information is in the patterns. They have a high stiffness-to-weight ratio,

because of which they have been extensively used in lightweight applications and additive manufacturing.

Six patterns were selected for creating the dataset. The dataset was generated using a parametric mode of the patterns, and a range of variables was determined to ensure the good quality of the data in the dataset. After the generation, the next step was to ensure sufficient data quantity and pre-process the data for import into the AI model. Sufficient quantity was provided through data augmentation through geometric transformation, and data reshaping and normalization were carried out to pre-process the data. The dataset was then split into a training dataset (80%) and a validation dataset (20%).

Training data is the data the model uses to train, and the validation data is used to check if the mode has been trained correctly.

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Fig. 3: Research Framework © Namrata Baruah

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.

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.

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Due to the time constraint of the thesis, that option was selected to move forward with the rest of the workflow.

From the generated images, new patterns that were not part of the dataset were selected for use in the application workflow.

The workflow consists of 4 stages. Stage 1 and 2 involves extracting the pattern from the image to a parametric environment and creating the shell form. Stage 3 and 4 involves morphing the patterns onto the generated shell and performing a preliminary FEM analysis on the final shape.

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.

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Fig. 6: VAE Results © Namrata Baruah Fig. 7: Comparison of generated Images © Namrata Baruah Fig. 9: Aplliction Preliminary Structural Analysis © Namrata Baruah Fig. 8: Application Workflow © Namrata Baruah

Conclusion

The thesis workflow can be summarised in the diagram in Fig 10. The steps involve inputting a selected dataset to the VAE model, selecting random samples from the latent space after training, decoding the results and importing the results to a rhino/grasshopper workspace, and finally, the topology exploration with a preliminary structural analysis. Of all the steps, the designer controls the input dataset, the selection of the random samples, and the exploration and deriving conclusions from the outputs. But for future exploration, another essential factor could be latent space interpolation.

The main conclusions and points of discussion of the thesis are as follows:

1. Dataset: the resolution of the images was 64 x 64, which resulted in output images of the exact resolution, which can be difficult to extract detailed patterns from in the application workflow. Hence that can be increased of other formats of data can be used.

2. VAE architecture and training: The model used worked effectively for the time frame of the thesis. However, there could be more complex models, e.g., hybrid VAE or GAN,

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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 structures. Originally 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.

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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

At the core of this vision is a decentralized network of computational solutions where no single actor is in control. We envision a network of AEC stakeholders providing automated computational solutions to one another, running on various cloud platforms.

"Any task that is currently conducted manually and has an element of repetition is a candidate for conversion into a computational solution."

These solutions involve computer processes that take input data and produce output data, in addition to other non-digital actions. However, developing such solutions from scratch would be difficult for most companies. Therefore, we expect that various platforms will emerge for creating and sharing solutions. User-friendly visual programming tools will be provided for stakeholders who want to create solutions. These tools will be similar to today's parametric modelling tools but will be available on the web and will have more advanced support for a suite of web services, including various AI tools with access to online repositories of training data. Marketplaces will also emerge where stakeholders can freely share or buy and sell solutions using various revenue models, similar to 'app store' models.

AI in the AEC industry - services and products

Within the AEC industry, there are two key types of companies: providers of services and providers of products. Providers of services include all types of consultants, such as architects and engineers. These companies can provide many of their services in a computational and highly automated form, with only nonstandard cases requiring manual human intervention.

Providers of products include all types of manufacturers and fabricators of building systems and components. For these companies, fabrication-aware online configurators can be used to generate customized product models tagged with the necessary data, or even to trigger complete file-to-factory manufacturing processes. For both services and products, the solutions that are created can either be interactively used by humans through some appropriate web-based interface or called in an automated way by other upstream solutions.

A composable generator of building constructions

By allowing solutions to be composable, an ecosystem of solution makers can emerge where solutions can be easily created by connecting existing smaller solutions. For instance, consider the possibility of a solution that connects three other solutions: the first generates an optimised massing for an apartment building based on various performance criteria; the second calculates a building cost by generating a detailed BIM model; the third predicts the sales prices of the apartments. The building massing solution could call various sub-solutions to predict building performance based on existing building data. The cost solution may call various product configurators for building systems such as walls, facades, roofs, etc. These configurators could be provided by the fabricators of those systems and could generate detailed BIM models together with cost information and delivery schedules. The sale price prediction solution could use historical sales data combined with detailed information from the BIM model and local neighbourhood to predict the price of each apartment. For all these solutions, we can easily imagine how AI and big data could be leveraged to improve their speed and reduce their error margin.

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One area of AI that has recently attracted a lot of attention is deep learning, which is evolving at a rapid pace. Many companies now offer deep learning models for working with unstructured data, such as text, images, videos, and audio. For example, Microsoft-owned OpenAI is behind the well-known GPT family of models that generate text, as well as the family of DALL-E models that generate images. However, in many domains, including the AEC domain, much of the most important data is structured data. BIM models, for instance, are highly complex examples of structured data. Developing deep learning models for such data remains a significant challenge. When we asked ChatGPT about this, it explained that structured data contains complex dependencies and requires domain-specific knowledge, both of which are difficult for deep learning models to handle. Despite this, research on deep learning with simple structured data, such as tabular data, is making significant progress. Additionally, advancements in the model training workflow, in which specialised models are trained as specified versions of large (near-) general AI models also show promise in resolving these limitations. We can expect these challenges to be gradually overcome, enabling the tackling of problems that involve structured data.

Black box character as a concern

In response to the recent advancements in AI concerns have arisen on the speed of development and whether the required amount of (ethical) control is being exercised. A major concern is the alignment of AI models; to what extent the goals and actions of the AI align with the intent of the human user, and how to test for this quality. This issue is a consequence of the black box nature of current AI, in which the user cannot interpret why or how the model has come to the outcomes it provides. The proposed decentralized model aims to alleviate the black box problem in multiple ways.

How to gain trust of the users

For solution users, the granular nature of the solutions means that the big black box of AI is avoided, and humans remain firmly in the loop. Admittedly, one might argue that the big box is replaced by thousands of smaller boxes. However, these smaller boxes are performing much smaller tasks that can be much more easily verified and checked. As such, it also becomes much easier to develop trust in these solutions. For users, this trust will likely initially focus on the company providing the service. If a well-known and respected engineering firm is providing a solution for evaluating a building’s energy performance,

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then the user is more likely to trust the results that are generated. But in case there are doubts, the user can also test out some energy analysis solutions provided by other engineering firms, to verify that the answers are similar. In this way, users are not forced into blindly accepting the correctness of one AI but are instead able to take a more critical stance.

Machine-learning as a service

For solution makers, the composable nature of solutions lowers the threshold and makes the process of creating solutions much more accessible. For instance, instead of attempting to create their own machine-learning algorithms, solution makers can utilize services that will run machine-learning models for them with their own training data. Trust in the company providing the solution is again crucial. If the company has a proven track record in developing machine learning models, then the solution maker is more likely to trust the results' quality. Moreover, if the company advertises its use of these trusted machine-learning solutions, it will likely increase its own trustworthiness.

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

buildings are used, which in turn will change what is considered ‘good’ building design. Additionally, human socio-economic behaviour is becoming more unstable, often triggered by environmental and health crises. This will further intensify the need for buildings to adapt, eventually leading to new ways of constructing buildings that enable just-in-time reconfiguration. All of this will require a never-ending supply of new types of computational solutions, delivering products and services that we cannot yet imagine.

latforms of solution makers

The development of a platform for solution makers

In this vision, the emergence of a rich and diverse ecosystem of solution makers and users is key. Within the AEC industry, we still have a long way to go. The first step is to create platforms that support such ecosystems. At Packhunt, we are working on such a platform. We are interested in your vision and opinions on the AI development in the AEC industries, so please do not hesitate to get in touch. If you would rather like to get some hands-on experience, join our early-adopter program!

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Maarten Mathot is Solution Specialist at Packhunt. His background is in Civil Engineering at Delft University of Technology, and his primary interests include parametric design, experimental software development, KnowledgeBased Engineering and other emerging technologies.

Patrick Janssen is Head of Research at Packhunt. He holds a PhD in design optimisation and has over two decades of experience in academic research and education into Building Information systems and performance-driven design at the National University of Singapore, University of Melbourne and The Hong Kong Polytechnic University School of Design.

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Maarten Mathot @Packhunt Patrick Janssen @Packhunt

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

The course setup involves and integrates 5 modules as follows:

Design Studio

This module focuses on integrated design development. It aims to integrate the different disciplines in an architectural context and output an extensive project which addresses all of the course learning objectives.

Programming

This module focuses on the knowledge and skills in programming, which are needed for the entire design process. It includes a series of lectures and exercises on programming (Python) fundamentals. What is presented in these sessions are principally sufficient for a successful outcome in terms of programming skills. However, It is allowed and encouraged to go beyond the content of the lectures and experiment with different programming languages, tools or methods.

Materials & Structures

This module focuses on the successful integration of know-how and techniques on Building Materials and Structural Design into their projects. It focuses on efficient and environmentally friendly solutions which are needed for the proposed architectural approach.

Design of Construction

This module focuses on (pre)rationalization of the design concepts towards construction challenges. It invites the students to develop not only creative but also realistic solutions, which use and develop efficient, sustainable and innovative construction technologies.

Tools, Methods, and Inspirations

This module includes a series of lectures and debates on several topics which can inspire or influence the students towards the learning goals of the course. It includes presentations given by guest lecturers.

Project: alt-r

The project proposed an alternative reality for space scarcity in cities, lack of proximity to public transportation, and centralized cities. It also questions the use of land and the static way of building of nowadays. The objectives were creating a network of user mobility and transportation means, creating a dynamic building system that can adapt to the continuously changing world, creating a modular and reusable structure, and creating a user-centered building that embraces adaptability to different needs.

The Delft Campus station was chosen for the project due to the large potential of creating a new centrality in Delft. As starting point, an analysis of the percentages of required functions was made to create the building's program taking into consideration the current surrounding activities and the inhabitants' change over time.

The architectural design was based on the voxelization of the space to create a base to populate the functions needed. A voxel is a mathematical unit in space that later gave place to the pods' occupation. It has a volume of 6.5x6.5x6m. The starting point was set up at the maximum boundary height according to the structural requirements of having a structure that could handle movements of change. Then some voxel extraction was made to adapt

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Fig. 3: Manufacturing model of alt-r Fig. 2: Design strategy of alt-r cluster

to the site context, such as adapting to catch more sun in the winter and avoid housing occupations in noisy spaces. The vertical circulation was placed according to fire safety distances and from it, the horizontal circulation was generated as starting point of the functions occupation. This placement was generated by generative aggregation rules per floor. An exact amount of each function unit was given to the algorithm to then place all of them.

The main objective for the structural design of this project was to determine the maximum height of this hub as well as the optimal cross-section sizes the design a modular structure. The main challenge for this flexible infrastructure system is the fact that it is constantly changing/moving, and it is hard to plan for that structurally. For this reason, we compared different loading scenarios of the different functions and took the highest one as well as simplified some functions to limit the loads. In addition, we also calculated the worst-case scenario where each voxel of the hub is filled with a pod.

Based on research we chose steel as our selected material for our structure due to its structural performance as well as its suitability and durability for a flexible structural system. In addition, we selected timber for the pod due to its lightweight properties, bio-based nature, and the healthier indoor climate it will provide to its users.

The growth of alt(r) is made possible in sections. Each section consists of a few grids, which then make it possible to flexibly distribute different pods and different functions possible over the years. However, even if a part of the grid is not being used in one location anymore, the steel connections can be cut apart. Since all the hubs are

constructed in the same way, the beams and the columns can then be reused easily in other locations. In addition, the pods themselves can be transported to another location, which increases their flexibility and prolongs their lifespan. The loop of reusing the construction members and finally the material itself is therefore endless.

Project: Hello,Boat!

The project aims to tackle the current waste management situation in the center of Delft by adding a second layer of transportation to the city's infrastructure. Currently, residents of Delfts center are deposing their household trash early in the morning on specific days on the curbside. Garbage trucks then maneuver through Delft's narrow streets to pick up trash bags, causing congestion and noise pollution. The vision of "Hello, Boat!" is an autonomous fleet of boats with modular trash cans. The residents can freely decide when and what type of waste to discard around the clock. Several hubs on the canal edges can be used to order a vessel arriving in less than 3 minutes. Boats with full capacities will return to a waste facility where they are emptied and maintained. The concept is not limited to waste management but has potential applications in cargo, passenger transportation, and the construction of temporary structures.

Since Delft is striving for a car-free center by 2040, the utilization of Delft's canals will contribute to a less crowded city, a reduction in noise pollution, and lead to user-centered waste management. The idea is based on the precedent of ROBOAT, an MIT/AMS research program on self-driving floating vessels in metropolitan areas. The

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team faced three challenges in developing this project. 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 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

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Fig. 4: Redesign of Roboat for carrying modular containers

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Fig. 5: City center route strategy

vessels into operation within the next 5-10 years, pending advancements in technology and legislation.

Unpack

Unpack aims to solve the package delivery problems of Delft with an easily scalable system, while trying to reimagine the future of shopping. Unpack is a platform technology, incorporating multiple aspects of the delivery logistics from packaging to delivery, from urban scale logistic networks to local social networks, and from material supply chains down to the individual design and structure of each local delivery hub.

Design Algorithms and Rules

A crucial part of the Unpack system is the physical delivery hub placed in each neighbourhood. 100’s or 1000’s of these need to be designed for each city and algorithms were developed to handle first the space plan

layout followed by the structural design together with the material kit-of-parts.

In Unpack the structural approach for the delivery hubs differs from a traditional Grasshopper parametric design. Instead of defining boundary conditions for the parameters, we defined rules and logic conditions for structural design decision-making. This required using loops to iteratively apply the rules and logic to the structural design. Loops, which are not a standard Grasshopper feature were handled by the plugin Anemone. Through this approach, our designs are not bound by a predefined parameter space, but instead develops iteratively following a set of rules that can be added to or modified. This also means we do not predefine the possible topologies of the structure, meaning the number of columns and shear walls, instead the rules define the conditions for placing each structural element. This allows the Unpack structural algorithm to handle any delivery hub design of any size.

Step-by-Step Structure Explained

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Fig. 6: Structural Design

The structural algorithm starts with a layout of the primary and secondary roof beams, as well as 3 randomly placed initial columns. The optimization of the structure then consists of 3 overarching steps: First, identify the point on the structure with the maximum displacement across all the load cases. Second, analyse the displacement and decide between the available structural elements; column, shear wall etc. while considering the available fixing points in the structure. Third, repeat if the exit conditions for structural stability are not met.

The initial random columns are removed one by one as new optimised support locations are identified. Each step can be modified independently, making for a modular set of rules and logic that can be easily improved. For the current simpler implementation only columns and shear walls are used. Additional elements such as slanted columns and cables were also investigated but were not included in the current kit-of-parts. The materials in the kit-of-parts determine the structural element properties such as stiffness and strength, but also the joint fixing conditions and stiffness, which heavily influences how bending is transferred through the elements.

This rule-based approach was chosen because a simple parametric approach would limit the possibilities of the final structural layout and it suited the open-endedness of the Unpack platform – ready to adapt for any logistics network of any size.

Studio Supervisors

Charalampos Andriotis

Frank Schnater

Hans Hoogenboom

Serdar Asut

Sevil Sariyildiz

Stijn Brancart

alt-r

Adalberto de Paula

Alina Wagner

Daniela Martinez

Julia Gospodinova

Lotte Kat

Talal Akkaoui

Hello, Boat!

Eva Schoenmaker

Marialena Toiliopolous

Nathanel Tzoutzidis

Patrick Ullmer

Pranay Khanchandani

Sarah Droste

Unpack

Job de Vogel

Lisa-Marie Mueller

Henk Rusting

Sebastian Stripp

Jirri van den Bos

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ALDOWA Join our Team, Become a Façade engineer! Email us at INfo@aldowa.nl

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Chart December 2022 to April 2023

Academic Year Event

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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!

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2022-2023

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.

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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.

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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

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Samanwita Ghosh Chair of Media and Secretary

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

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