computational design in architectural process

in architectural in architectural process process

tomáš bajer

VIA UNIVERSITY COLLEGE, CAMPUS HORSENS ATCM · AH41-23S · 4TH SEMESTER EPE-REPORT SUPERVISOR: STEEN FYNBO LARSEN 12 · 01 · 2024

approach to computational computational design design

Title leaf

Report title:

Approach to computational design in the architectural process

Author(s) and student number(s):

Tomáš Bajer, 341961

Consultant:

Steen Fynbo Larsen

Number of pages (2400 keystroke/page):

47 936 characters ~ 20 standard pages

I (all authors, if more) hereby confirm, that this report has been made without receiving unauthorized help cf. Government order no. 1500 of 2/12-2016 § 20,2 cf. §20, 1 and 2.

date and signature: 12th January 2024

Computational design in the architectural process, Tomáš Bajer, 12.01.2024

FOREWORD My profound appreciation belongs to Ing. arch. Šimon Prokop, Ing. arch. Lukáš Kurilla, Ph.D. Ing. arch. Pavel Jurčík, Ph.D. and Ing., Ph.D. Martin Petřík, who guided me through the extensive world of computational design with knowledge and motivated me to explore this area further. I would also like to thank my supervisor, Steen F. Larsen, who supported the topic idea despite the connected challenges.

ABSTRACT The co-design approach of an architect and a computer brings opportunities to shift the creative process towards more sophisticated forms, effective variant iteration and solution exploration.

Computational design can be utilised to approach complex challenges systematically, automate repetitive tasks, or act as an efficient design explorer. The architectural process can use computational design effectively both in creative ways of early-stage prototyping and further concept development - if approached systematically, with consideration and overview. Through a combination of theoretical knowledge and empirical data are analysed aspects of creative development and possible approaches to co-design. How do human design principles relate to parametric, generative, deep learning or algorithmic design methods. How aspects of our workflow can be communicated to the computer with relevant context and abstraction. How to set our evaluation methods and effectively explore developed concepts, further mapping possible approaches of utilising these methods within our existing architectural process.

Keywords Computational design, optimisation, design loop, co-design, parametric architecture, generative architecture, neural networks

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TABLE OF CONTENTS Front page ......................................................................................................................................... 1 Title leaf ............................................................................................................................................ 2 Foreword........................................................................................................................................... 3 Abstract............................................................................................................................................. 3 Table of Contents .............................................................................................................................. 4 1 · Introduction ............................................................................................................................. 5 1.1. Problem presentation ......................................................................................................... 5 1.2. Problem background .......................................................................................................... 5 1.3. Problem statement ............................................................................................................. 5 1.4. Delimitation ....................................................................................................................... 5 1.5. Short presentation of theoretical basis ............................................................................... 5 1.6. Short presentation of empiri .............................................................................................. 5 1.7. Structure and Reading guide.............................................................................................. 6 2 · Theoretical basis ...................................................................................................................... 7 3 · Methodology ........................................................................................................................... 8 3.1. Collection of Empirical data .............................................................................................. 8 3.2. Scientifically Philosophical Approach............................................................................... 8 3.3. Reflections on the validity and durability of empirical data ............................................. 8 4 · Empirical data ......................................................................................................................... 8 4.1. Presentation of Empirical Data .......................................................................................... 8 4.2. Treatment and Analysis of Empirical Data ........................................................................ 9 5 · Analysis ................................................................................................................................... 9 5.1. Defining creative process .................................................................................................. 9 5.2. Approaching the computer............................................................................................... 10 5.3. Iterating towards solution ................................................................................................ 17 5.4. Abstraction and context ................................................................................................... 21 6 · Discussion ............................................................................................................................. 23 7 · Conclusion............................................................................................................................. 24 8 · Source list .............................................................................................................................. 25 9 · Figure and illustrations list .................................................................................................... 26

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Computational design in the architectural process, Tomáš Bajer, 12.01.2024

1 · INTRODUCTION 1.1. Problem presentation „The machines will take our jobs“ is a phrase that humanity has been dealing with for a few centuries – yet here we are. Technological advancements have not replaced us but rather provided tools to work differently, more effectively and allowed us to focus on what actually matters. The same technological shift is happening in architecture and the creative process behind it. So, how can we approach a machine to co-design with it?

1.2. Problem background The architectural world is actively seeking new concepts to move forward and, simultaneously, being pressured into adapting to new technology. The persistent increase in computing power allows us to explore designs through various form-finding approaches, generative methods and optimisations. To understand its potential in both creating new forms and iterating over designs more effectively, we need to consider the nature of these approaches and how they embed within our architectural process. When utilised correctly, these methods can bring new insights into design evaluation and creation, bridging abstract concepts and successful designs.

1.3. Problem statement How can we approach architect-computer co-design in concept prototyping?

• •

How does creative development relate to computational design principles?

What are the challenges, potentials and barriers to these approaches?

1.4. Delimitation The concept development methods in architecture are vast and often interconnected with different disciplines. The main focus is on the principles of concept prototyping and the challenges encountered within this process in relation to computational design approaches. Defining the role of creativity or the term itself is omitted; only consequent processes are considered. The terminology within the field of computational design often differs in definitions, and the focus of this report is not to classify nor explain in detail the mentioned methods. Instead, mapping and discussing principles, analogies and relations of various approaches to co-design.

1.5. Short presentation of theoretical basis The theoretical literature allows us to orient within the extensive area of computational design and outlines the argumentation basis of methods, further providing descriptions of distinct features and possible use cases. Resources focusing on the psychological aspect of the creative process were used to delimit the focus areas and identify key topics.

1.6. Short presentation of empiri Empirical data based on multiple interviews with specialists in the field were used to explore different attitudes in-depth, mainly from the computational aspect. Individual experience and opinions based on applied research topics were particularly useful in defining the relations and problem-solving mindset needed to apply researched methods successfully.

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Computational design in the architectural process, Tomáš Bajer, 12.01.2024

1.7. Structure and Reading guide The central part of the report „5 · Analysis“ contains structured argumentation analysis from theoretical and empirical resources in four main sections. Each sub-headline progresses from abstract concepts towards more advanced approaches to computational design.

5.1 Defining creative process Discusses psychological and philosophical aspects of architectural concept prototyping and possible analogy towards computational methods 5.2 Approaching the computer Maps four fundamental concepts of computational design, each reflecting architectural thinking and analysing various opinions about methodology, workflows and mindset for utilisation.

5.3 Iterating towards solution Exploring evaluation possibilities and challenges present in the iterative nature of the architecture.

How to tackle non-linear approach, effectively define and analyse solution space.

5.4 Abstraction and context What context needs to be communicated and what omitted. How do we simplify the reality in order to work with it effectively while staying relevant.

The following section, „6 · Discussion, “ focuses on the present and future of computational design through the subjective perspectives and opinions of interviewed specialists.

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Computational design in the architectural process, Tomáš Bajer, 12.01.2024

2 · THEORETICAL BASIS Theoretical literature in the context of computational design, consisting primarily of scientific studies with specific use-cases of computational design, provided a practical approach overview, detailed method specification and terminology.

Relevant resources discussing aspects of architecture, the cognitive and behavioural concepts and processes that underlie creativity, enabled a deeper understanding of human-centred design and analogy towards computational approach.

AI + Architecture Towards a New Approach (Chaillou, 2019) Master thesis demonstrating architectural feature synthesisation and floorplan generation through a pipeline of specialised GANs from building footprint to furnished floorplan. Presents selected solution spaces graphically through an interactive tree diagram.

Computational design in architecture: Defining parametric, generative, and algorithmic design (Caetano, Santos and Leitão, 2020) Critically examining the existing terminologies associated with computational design, the study proposes a refined and consistent taxonomy for these crucial concepts.

Generative urban design using shape grammar and block morphological analysis (Wang, Song and Tang, 2020) Addresses challenges in contemporary urban design by proposing a framework of generative grammar of urban blocks with a hierarchical structure.

Meta-Parametric Design (Harding and Shepherd, 2017) The authors propose a novel approach combining graph-based parametric modelling with genetic programming. It is helpful for analogy as showcased design exploration methods more closely resemble the human creative process while retaining the benefits of parametric scripting.

A generative architectural and urban design method through artificial neural networks (Zheng and Yuan, 2021) Characteristic features of architectural form synthetisation through neural network.

Shape Finding or Form Finding? (Goldsmith and Ap, 2014) Discuss the differences in the creative process of shape and form finding approach through modern materials and structural technologies.

Architectural Thought: the design process and the expectant eye (Brawne, 2003) Explores methodological approaches and motivations within architectural processes, discusses psychological, philosophical, and contextual perspectives.

Psychology of Design (Carbon, 2019) This paper discusses the comprehensive integration of psychological dimensions within the general design process to systematically consider human perception, emo-cognitive processing, and psychological effects.

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Computational design in the architectural process, Tomáš Bajer, 12.01.2024

3 · METHODOLOGY 3.1. Collection of Empirical data Data were collected through three interview sessions with professors Ing. arch. Lukáš Kurilla, Ph.D., Ing. arch. Pavel Jurčík, Ph.D., Ing., Ph.D. Martin Petřík and Ing. arch. Šimon Prokop, all related to the faculties of architecture and civil engineering of Czech Technical University in Prague, each experienced in various areas of computational design. Sessions were carried out as semi-structured discussions, characteristically comparable to brainstorming. Prepared topics were presented with contextual information to induce relevant discussions. Sessions were not recorded at the interviewees' request. Therefore, extensive notes and direct citations were made during the process, which were extended and reformulated afterwards. During the report, summarised usecases of computational design are presented, however because of their origin from reliable science articles, are not considered as empiri.

3.2. Scientifically Philosophical Approach The most relevant topics are architectural concept workflows linked to different methods of computation design. As the normative approach would not provide a relevant view of possible methods, it has been abandoned. The provided opportunistic opinions based on both theoretical and practical knowledge are considered to be the most suitable for the report.

A more extensive case study would be helpful to explain the different decisions undertaken during the selected project, but as to the report's limitations, summarised case studies are preferred.

3.3. Reflections on the validity and durability of empirical data As the interviews were done with professors academically experienced in the general topic, the collected information is considered as qualitative empiri. The data is helpful at both delimiting the contemplated areas and providing enough consistent insight to perform the assessment. Opinionbased data suggesting various design and computation approaches were, as long as relevant, considered and utilised within the report based on their professional background source.

4 · EMPIRICAL DATA 4.1. Presentation of Empirical Data The interviews were preceded by preliminary discussions with the professors, which helped to formulate and delimit the topics and guide the interviews into more open discussion, replacing the originally intended structured questionnaire, which could potentially cause irrelevance both in the answers and in the individual's expertise.

The topics for the full interviews were based on defining aspects and relations between design and computational principles (contemplated relations marked with “~” character). Considered areas were: Design loop ~ iteration, abstraction ~ context, black box, evaluation ~ optimisation, guidance ~ co-design, cultural values, authorship

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Computational design in the architectural process, Tomáš Bajer, 12.01.2024

4.2. Treatment and Analysis of Empirical Data Empirical data are, as argued above, of a qualitative nature, which allows for the assessment of central key points and the analysis of presented opinions from different interview sessions. The data was summarised into a non-structured series of notes during the discussions, allowing us to capture critical points within the vital context and flow of thought. After each session, the notes were reorganised, relevantly merged and further extended into topic-specific paragraphs. Finally, translation and semantic paraphrasing took place to formulate structured argumentation.

5 · ANALYSIS The architect's role is crucial as his flow of thought is transformed into a real spatial environment, which surrounds us for decades with very limited options for additional modifications. For this very reason, the architectural approach to designs should not be „trial and error“ but rather an iterative, refining design process with creative „flare“ moments and thoughtful, focused considerations.

When approaching computational design methods, it is, in multiple cases, possible to adapt and reflect our principles in the selected workflow—further allowing for a blend of computational efficiency with human intuition, aiming for a collaborative and exploratory design process.

5.1. Defining creative process To elaborate on how general principles of architectural prototyping can relate to computational design methodology, we first need to define what the phrase „to create something“ actually means.

The creative process has been studied by philosophers, psychologists, and artists for centuries.

There is no single, universally accepted definition of the creative process. However, it is generally understood to be a nonlinear and iterative process that involves a series of stages, from preparation over incubation to illumination and verification.

The objective in this context is shaping creativity through relevant methods towards designing coherent, unique, and functional environments. Thinking through variants, reacting to context, working through abstraction, flexing boundaries, and exploring nuances. These are some of the principles generally considered to provide both insight and an overview of the objective.

Computational design has the potential to incorporate these abstract keywords, even though the method or approach changes gradually.

5.1.1. Design loop From a psychological perspective, it encompasses divergent and convergent thinking, allowing architects to generate a variety of ideas and then refine them into a cohesive solution.

Methodologically, it relies on iterative cycles, where feedback mechanisms followed by some form of adjustments contribute to the evolution of design ideas.

The same reasoning applies to even the most sophisticated computer design. The concept starts growing from a certain point, scheme, or concept, utilising certain parts with connections and gradually locking segments and iterating further. Selecting solutions and moving one step further.

It is always an iterative process, and we should approach it accordingly (Kurilla and Prokop, 2024)

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Computational design in the architectural process, Tomáš Bajer, 12.01.2024

Figure 1 Analogy interpretation between human and computational design loop appended drawings from the personal archive for demonstration purposes.

The analogy between sketch and parametric concept development within the design loop can be described through Figure 1 as (a) the beginning of the loop with objectives, communicated through geometrical basis, relations or numerical parameters, (b) processing of the inputs through selected design method (c) generated conception (d) evaluation through further processing and analysis. In the upper situation through, room development and followed by empirical analysis of layout fitness, in the lower by reactivity to wind simulation. Finally, a loop back to refine the input or, eventually, the process itself. (Harding and Shepherd, 2017; Carbon, 2019) The crucial concept is that the theoretical solution space is nearly infinite. Nevertheless, architects are able to navigate diverse ideas within the cognitive landscape by often indirectly utilising a form of limiting the variants and narrowing down the explored range. This can be done by following intended objectives, methodology, relations, conceptual forms or functional areas. (Brawne, 2003) In the computational design field, these principles can be similarly utilised, either as an input parameter, as an intended design process constraint or inside the automatic evaluation.

The aspect of the human design process that is challenging to implement into computational approaches is the non-systematic nature of our minds. A hardly predictable combination of divergent and convergent flow of thought guided by our inner feelings and experiences. Altering exploration and focus often enhances the design's nuances, breaking and flexing previously set boundaries and, therefore, shifting the overall concept. The introduction of diverse approaches to specific design segments often breaks the cohesion but temporarily flexes the boundaries. In other words, it widens the solution space, allowing for selecting a path that potentially leads to a more successful design. (Carbon, 2019)

5.2. Approaching the computer When approaching the computational methods, we can figuratively think about how our concept of a virtual model is defined. Not to be confused with a digital model - rather as a computational representation of our intentions and design process.

Consider what does suit the expectations and approach. To what extent is our virtual model defined, and, more importantly, how it can be manipulated? When working with parametricism, it is a 10 · 26

Computational design in the architectural process, Tomáš Bajer, 12.01.2024

variable – a parameter of the model which we can change; with genetic algorithms, more complex chromosomes combining these variables. With the neural network, it, in a sense, means manipulating it as a whole. If we can technically dive into the inner workings, the source can be the full parametric script, made, for example, in Grasshopper” or complete neural network architecture, which we can modify or retrain. (Kurilla, 2024) Finding a balance between prototyping and perfecting the design is an essential part of the process.

Having a structured and clear data structure will allow for extractions and collaboration in the future. Not only focusing on designing the most intricate solution but also assessing manufacturability, functionality and overall solution relevance. Why spend weeks perfecting our parametric facade when we did not consider the internal structure or spatial requirements? Keep expectations realistic and critically consider designs before putting enormous effort into developing stunning but unbuildable prototypes. Knowing your skills, design features and buildability limits kind of a certainty calibration. (Petřík, 2024)

Figure 2 Example of single-objective utilisation of parametric approach for architectural purposes(right). Visually, perhaps appealing form of steel welded truss inspired by voronoi (left), but the geometrical qualities of this principle are not utilised apart from aesthetic purposes. Manufacturing of such element would be highly challenging (author's personal archive, Ostravar arena outline, 2022)

As a medium of explanation, the software Grasshopper embedded within modeller Rhinoceros is used throughout the report. Grasshopper operates through a node-based interface, allowing users to create algorithms by connecting parameters, geometrical references and components through a visual scripting approach. A data-flow model is employed, where information flows through interconnected components representing different functions ranging from mathematical operations to complex spatial algorithms. Its principles involve a modular design process allowing for iterations in real-time, fostering a dynamic and iterative approach to parametric modelling.

(Harmon, no date) Grasshopper is not by any means strictly a parametric tool; rather, it should be viewed as a platform to incorporate and combine a wide range of design methods. Some of the following approaches to computational design are presented through this environment to provide a consistent view.

Computational design is a powerful approach that allows architects not only to create forms differently, but with appropriately structured data, also directly include analyses, optimisations, and technical aspects in the design. That is a significant game-changer with increasing competitive and legislative requirements. Architects, amongst many others, are gradually becoming also programmers because well-implemented technology can be both efficient and creative. Allowing not to waste time on low-end repetitive tasks but moving directly to the design side with freedom of creation. (Kurilla and Prokop, 2024)

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Computational design in the architectural process, Tomáš Bajer, 12.01.2024

The overall terminology of methods of computational design and approaches of shape-finding and form-finding are in literature often used loosely with and defined with overlapping features.

(Caetano, Santos and Leitão, 2020) Here are used as a supportive description for distinguishing between different methodological approaches and mindsets towards computational design

5.2.1. altering the concept through parametricism The parametric approach shares similar features with systematic shape-finding and concept morphing – for exploring alternatives. (Harding and Shepherd, 2017) The basis of the concept is a parameter, a variable input we use for manipulating the result in an arbitrary sense; therefore, it does not have a strict form – it could be, for example, a vector, numerical range, sun path or a geometrical object. By interconnecting these inputs with our components, we might create or morph elements based on interaction, calculations, modelling operations or algorithms. (Caetano, Santos and Leitão, 2020) Creating dynamic relationships within defined constraints drives the generation of complex, variable and reactive forms. The methodology often involves scripting and coding, enabling designers to control and manipulate design elements algorithmically. (Caetano, Santos and Leitão, 2020) These constraints usually geometrically signify the solution's origin from a single concept. So, without rebuilding the generation script, we can achieve only quite a limited and narrow solution space. (Prokop, 2024)

Figure 3 Parametric script with geometrical and numerical inputs generating structural frame for glazed fluid surfaces, Grasshopper script (top), resulting geometric results (bottom) (author's personal archive, academic studio project, 2023)

Basically, creating architecture as a system, where we undergo idealisation followed by composing the elements and seeking logic within them. After that, optimise the design or approach and refine aspects. The iteration that can continue with evaluation towards reaching a more fitting solution (Kurilla, 2024)

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Computational design in the architectural process, Tomáš Bajer, 12.01.2024

We can consider Jean Nouvel a parametric architect as he thought about his urbanistic concepts through numbers. Rebuilding and transforming the layout of public space based on how different paths of pedestrians were in response (Kurilla, 2024)

5.2.2. evolving the concept Evolutionary algorithms inspired by the principles of biological evolution complement the parametric approach by providing a systematic way to explore and optimise the parametric design space by evolving the most fitting solutions. Based on predefined feedback criteria, they mimic the natural selection and mutation process to iteratively optimise the design. (Caetano, Santos and Leitão, 2020) The algorithm does not evolve in a sense that would introduce new features through mutations. It simply modifies parameters so the evaluation converges towards local maximums. Creating a set of designs, evaluating the most suitable ones and morphing their parameters to create the next generation. The level of detail is constant, only its features optimised based on the numerically expressed fitness value. (Rutten, 2010)

Figure 4 The connected model in Grasshopper, optimising spatial voronoi form, 3D representation (left), Grasshopper source script (middle), converging optimisation process graph (right), (APDesign Videos, youtube.com)

The principle uses a parametric model and connects it to the evolutionary algorithms to optimise searching through variants. However, the parametric model has to have the sought solution somewhere within its constraints, and the fitness function should be able to evaluate it. Otherwise, the optimisation might not provide a meaningful solution or converge to any at all. (Kurilla and Prokop, 2024) If we wish to restructure the solutions, a change to the network is needed. It is possible to utilise interactive evolutionary algorithms, where the user selects preferred solutions in the generation process, allowing them to achieve different design optimisations by shifting to different local maximums. (Kurilla and Prokop, 2024)

5.2.3. breaking the boundaries with the generative approach Generative design aligns with an emergent design philosophy, similar to the form-finding approach, emphasising that optimal solutions can emerge through iterative adaptability and contextual responsiveness. The idea embraces that innovative and unexpected design solutions can evolve through algorithmic exploration. (Carbon, 2019; Caetano, Santos and Leitão, 2020) 13 · 26

Computational design in the architectural process, Tomáš Bajer, 12.01.2024

Figure 5 Antoni Gaudí, An upside-down force model of the Colònia Güell, Sagrada Família Museum (Canaan via Wikimedia Commons)

An interesting analogy to the generative form-finding predecessor is Antoni Gaudí, who utilised the upside-down string-weight force model already in the initial design stage. Just by defining external conditions through position and weights, gravity created the catenary geometry for him, providing him with amazingly precise results in the initial phase. By setting up a generative or parametric system that searches for the optimum, we can playfully change the elements, constraints, and context while receiving real-time feedback. (Kurilla, 2024) We can tell the generative approach from others by how it works with inputs. It uses these variables as initial context and finds a way through predefined logic towards these inputs to the extent that it essentially changes the rules of how things are generated. Allowing to generate various branches of solutions, as it enables more impactful variable alteration of rules through chromosome composition. (Kurilla and Prokop, 2024)

Figure 6 Generative pattern as the support structure for the AM production, (Ren & Galjaard, 2015)

Figure 7 Generatively optimised column-beam system (Green, 2023)

Generative design is often utilised with the objective of finding and manufacturing structurally most effective forms of parts with high demands on weight effectivity, as depicted in Figure 6, for the field of construction might provide designs of less material consuming, therefore more sustainable structural systems as proposed on Figure 7, or optimising spatial supports in a similar manner.

(Green, 2023)

5.2.4. morphing and improvising using generative neural networks Neural networks, no matter how complicated, are just interacting sub-algorithms, and so far, most of them are of a deterministic nature. (Petřík, 2024)

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Comprising layers of artificial neurons with weighted connections, these networks learn from data by adjusting the weights during training. This enables them to recognise patterns, make predictions, or classify information based on the learned relationships between input and output data. The surprisingly complex nature of neural networks, characterised by the interconnected cooperation of their subsystems, can resemble the phenomenon of emergence observed in nature. (IBM, 2023) Neural network has the highest degree of freedom in solutions, as only its architecture is predetermined, and all the complex content and internal logic with weights are based on training.

It is highly responsive, as it can adapt to new challenges if they are at least somehow relevant to what it was trained on. (Kurilla and Prokop, 2024) A smaller artificial neural network was trained and utilised in Figure 8 for synthetisation resembling characteristic forms of selected architectural studios. The input surface bodies were reconstructed using control point manipulation through the network, with input neurons represented as quantified vectors to represent the design properties. (Zheng and Yuan, 2021)

Figure 8 Utilization of neural network for characteristic resemblance by NURBS body deformation „ (Zheng & Yuan, 2021), multiple figures from the original paper merged to capture a broader objective context.

Neural networks are able to react relevantly to inputs with high levels of abstraction - it can be creative, non-specific, or even contradictory, as long as it follows some basic assumptions. This aspect is particularly useful if the network has subsystems allowing for arbitrary input prompts - to be loosely structured, in the form of, for example, sentences, concept. Prompt can contain mixed 15 · 26

Computational design in the architectural process, Tomáš Bajer, 12.01.2024

combinations of data types, such as image, guiding geometry, and text, depending on specific network architecture. (Kozma, Ilin and Siegelmann, 2018; IBM, 2023) Generative design principles allow architects to handle complex tasks, explore vast design spaces, and process large amounts of variants more efficiently.

General practice, applicable for multiple kinds of genAIs, is again iteration and organised prompt layering. Beginning by defining a broad thing and gradually refining and evaluating results.

(Kurilla and Prokop, 2024) As a part of the input, we can provide a reference to the previous responses. This practice can help with context consistency, sort of self-reflection and reduction of unwanted deviations or repetition.

(Petřík, 2024) Too long a prompt can lead to a point where the neural network cannot distribute weights effectively and causes a kind of breakdown. It becomes less predictable and can be more creative but can also so-called „hallucinate“ unreasonable outputs. The breaking point is challenging to predict, but it is evident in the results. (Kurilla and Prokop, 2024) Another of many use cases is progressive object generation, which utilises deep learning based on population and detailing of the provided context. Figure 9 depicts an urbanistic approach, different layouts and corresponding rules were referenced to the network, which afterwards allowed for concept generation of structured blocks, areas and whole city parts (Figure 10). (Wang, Song and Tang, 2020)

Figure 9 Reference organisation modes according to the land (Wang et al., 2020)

Figure 10 Resulting generative adaptation, Buildings’ generative process and corresponding grammar structure, Generative urban design using shape grammar and block morphological analysis (Wang et al., 2020)

To train neural networks effectively based on their internal feedback loop, we can utilise Generative Adversarial Networks (GANs), which consist of a generator and a discriminator engaged in a competitive training process. This adversarial setup results in a dynamic equilibrium, where the generator continually refines, synthesising its output, and the discriminator enhances its ability to distinguish genuine and generated samples. By learning complex data distributions throughout this process, their application spans various domains, including computer vision, image synthesis, and data augmentation. (Creswell et al., 2018) 16 · 26

Computational design in the architectural process, Tomáš Bajer, 12.01.2024

Using this approach, a progressive floorplan synthesis (Figure 11) was utilised, emulating the architect's process. The pipeline encapsulates each step within a specific GAN model, trained for its respective operation. From the given parcel geometry to the building footprint, followed by room split, while respecting meaningful adjacencies, dimensions and layout programme. Further following with doors and windows fenestration to allow for views and circulation, and lastly, the addition of furniture based on room purpose. (Chaillou, 2019)

Figure 11 Multi-step pipeline to draw a floorplan using GANs (Chaillou, 2019)

genAI has extreme conceptual potential in the „flare“ phase of the design. Challenges come when we need it to focus on detail or maintain consistency throughout different inputs. It is a complex system that still does not understand the precise context of prompts, its own outputs, or connections within. And that is usually the space for a human. (Kurilla, 2024) The neural network creates through combinations of systems and morphing based of training data, but a meaningful architecture can emerge only if it can follow and observe connections and understand context. The challenge is also in describing the quality objectively and with reasoning.

(Prokop, 2024) Ontology serves as a foundational component for AI in architectural design, but is currently used mostly in GIS. It enables the development of systems that can reason, infer, and communicate based on the understanding of context. (Kurilla and Prokop, 2024) Ontology refers to a formal representation of knowledge that captures the concepts, relationships, and entities relevant to the objective, crucial for describing architectural systems, materials, and processes. Ontologies provide a structured framework for organising and understanding contextual information, with the potential to enhance interoperability between different software tools and support the development of AI-driven design assistants. (Hohenecker and Lukasiewicz, 2020)

5.3. Iterating towards solution In order for the design process to be considered as a closed loop, there has to be a form of evaluation which decides what variant or context will be referenced for the following iteration. This process 17 · 26

Computational design in the architectural process, Tomáš Bajer, 12.01.2024

can range from solely relying on the architect's momentary decisions to calculated fitness values based on measured design parameters. Whether the feedback after each iteration is provided by a human or a machine determines the speed of iterations, but also the level of direct control the architect holds about the process. Optionally, we can use interactive algorithms which allow for a combination of the mentioned (Harding and Shepherd, 2017; Carbon, 2019)

5.3.1. Defining when to stop It is unreasonable to prototype every aspect and parametrise every variable. We are still working with a simplification, a kind of abstraction, which allows us to create within an appropriate solution space and follow our objectives. (Petřík, 2024) An essential aspect of design creation is knowing when to stop the current phase– sketching layouts, modelling variants or prototyping through computational design methods – and move on. This is usually based on experience, limited by resources or time frame for the objective. (Brawne, 2003) Too complex virtual model or ineffective evaluation methods might cause multiple issues. We might be considering too wide solution space, which becomes challenging to optimise and evaluate objectively, not speaking of the resources and time it takes to generate. (Petřík, 2024) And if this wide range of variants relies heavily on human evaluation, it is a good idea to reconsider our approach. Human attention quickly decreases when exposed to amounts of similar solutions where nothing stimulates the brain. Suppose one wants to creatively yet critically explore relations, shift and modify design aspects. In that case, some systematic approach is needed, some filtering, pre-evaluation, not just clicking our way through everything. (Petřík, 2024)

5.3.2. The challenge of evaluation The ability to generate a successful design depends on defining whether the change is an improvement or not. Simple as that might sound, evaluation poses various challenges in setting up the interacting rules and defining them correctly. There are limitations or legislation that have to be strictly followed, such as structural stability, but many more that determine the quality of space, but can be bent or broken under the proper context. (Brawne, 2003) Floor plans are easily understandable to us, but how does a machine evaluate the structural and spatial qualities? When we skip apparent issues such as doors colliding with walls, we have many rules that are not strictly bound that apply only to a particular context or form. How does one communicate complex qualities to an algorithm when they can differ every time?

5.3.3. Multi-objective optimisation One often utilised approach is optimising multiple more easily definable qualities such as cost, sunlight, floor plan areas, different zone ratios or structural aspects. Multi-objective optimisation involves simultaneously exploring combinations of conflicting objectives or criteria and evaluating the design through the fitness of multiple combined numerical rewards and penalties. (Chang, 2015) This principle recalls the general philosophy that a singular, optimal solution may not exist due to the complexity of design requirements. Psychologically, it accommodates the inherent trade-offs architects often face in balancing various design considerations. The intent is to provide a way of narrowing down the solution space towards functional variants or allowing for an efficient orientation within it. (Carbon, 2019)

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Figure 12 Multi-objective optimisation with plugin Octopus for Grasshopper, a 3D graph of solution space (left), variant scores within optimised criteria (right) (Octopus plugin, food4rhino.com)

Figure 13 Plugin components corresponding to various phases and approaches (Octopus plugin, food4rhino.com)

Figure 14 Navigation through solution space with previews (Octopus plugin, food4rhino.com)

5.3.3.1. Setting up the weights Usually, we value one quality over another – for example, we consider the spatial effectivity of corridors more important than the size of balconies. Furthermore, objectives, such as apartment layout quality, are determined by multiple, often interconnected or contradictory criteria. (Chang, 2015) Therefore, we need to define which range corresponds to reasonable solutions, what happens when the range is not met and how the criteria influence the whole. Usually, it is done through numerical transformation functions (Chang, 2015) Setting weight combinations is challenging as it needs to work for various solution contexts. We need to pay attention to having a balanced solution space, combining weights properly, normalising criteria values, and remapping them to reflect rewards and penalties. However, we often tend to misjudge the relations between aspects; we might, for example, find new contexts where the qualities interact differently. So, it is generally a good practice to combine thoughtful considerations of weights with iterative modifications. (Petřík, 2024) 5.3.3.2. Navigating through solution space Our brains often resist information overload by distraction or lowering attention. This is not ideal when we need to stay focused; therefore, a more systematic approach is needed.

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Everything is about setting up workflows efficiently and ensuring the computational design results are relevant to our needs and objectives. A principle of „clustering“ is usually utilised to explore patterns and understand the relationships between different sets of design alternatives (Kurilla and Prokop, 2024) Clustering involves grouping solutions based on their proximity or similarity to each other (Figure 15). This approach helps identify and present a diverse set of solutions covering different branches of design and trade-offs between conflicting objectives. (Chang, 2015)

Figure 15 Solution space of results from GAN processing, relations described through the form of clustering - Similarity Tree. Design branches computers according to Program Metrics (Chaillou, 2019)

It is also possible to filter through a wider number of results by how they performed within each evaluated objective (Figure 12). This allows us to consider marginal, non-competitive, top or worstperforming solutions and concepts of optimisation output. (Kurilla and Prokop, 2024)

5.3.4. Evaluating beauty But can we objectively rate such a thing as the quality of a living space? The rating of aspects such as architectural aesthetics poses, quite expectedly, various challenges. As this criteria relies on individual perception influenced by context, experience or just general mood, it is simply not possible to measure directly. (Brawne, 2003) Beauty can be rated to some extent by a neural network trained on a database of curated images in terms of aesthetic-specific captions. Architecture is even more challenging as it functions as a system that is significantly harder to quantify in terms of aesthetics. Emotions and feelings can theoretically be indirectly derived from various secondary relationships, such as composition, spatial features and other similar derivations from general rules. (Kurilla and Prokop, 2024) However, a person can still serve as a valuable part of the evaluation function – with the role of the decision maker, determining where development should take place, and which branch of the design should continue. Aesthetics might be just shaped through human feedback, while the rest is computed (Kurilla and Prokop, 2024)

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5.4. Abstraction and context Abstraction in architecture resonates with the notion that simplicity can enhance understanding and appreciation. Emphasising the importance of distilling complex ideas into their fundamental forms or principles to allow sufficient idea communication and concept development. (Brawne, 2003) Why cannot we expect an architect or a computer to produce a satisfying result after the first design iteration, even though we have provided sufficient contextual resources and explicit intents? The idea behind is working through the designs causes the levels of abstraction to gradually decrease and the concept interpretation by our brains is shifting. Our initial intents, even though based on very specific needs, carry only abstract notions of the concept - for the purpose of imagining, expressing and communicating. When brought within the context, the increased specificity allows our minds to re-interpret and adjust either our expectations or the design to meet them more precisely. Our brains are not very effective at planning precise paths from blank to fully developed ideas; rather, they iteratively re-adjust during the process, providing easily graspable, more structured and logical development. (Brawne, 2003; Carbon, 2019) When we simplify into primary forms, we can surpass the context temporarily, which would otherwise overwhelm our ability of decision making. Without abstraction, we would be lost in details, being constantly challenged by all the aspects, inner workings and relations between each and every element. (Petřík, 2024)

Figure 16 Deduced graph of existing relationships among rooms (left). Idealised adjacency matrix, reporting these connections (middle). A graph representation visually easily perceivable for a human (right) (Chaillou, 2019)

The crucial concept is idealisation - a level of abstraction through which we communicate the problem. How much context do we strip away, which of it do we represent simplified, and what do we include as inputs to create relevant solution space? There is always a level of idealisation - even constructional details are simplified blueprints for reality; the critical concept is if they communicate relevant content. (Brawne, 2003; Carbon, 2019) In order for simplified models to be effective and relevant, they have to resemble reality as closely as the degree of abstraction allows. Furthermore, we must be able also to transform the findings back into the resulting design. The intensity of abstraction greatly determines our solution approaches, possible conflicts, but also opportunities (Prokop, 2024) The idealisation usually consists of reducing the geometrical complexity of elements, purging irrelevant contextual objects, and using only relevant characteristics and relations between 21 · 26

Computational design in the architectural process, Tomáš Bajer, 12.01.2024

elements. As an example, for critical window point sunlight analysis, we can simplify the overall detailing of façade elements, approximate complicated bodies, such as trees, with simpler geometrical volumes, remove all objects and surfaces of walls except the outer shell and provide only reflection-related properties of surface materials. This way, the calculation is still very relevant but also optimised and based on our approach, and, usually, easily modifiable. (Jurčík, 2023) Figure 17 showcases the generative development of an idealised series of connected circles, each representing a room, towards a scaled and organised layout. Certain features, such as corridor dimensions are omitted until consistent layout is not completed. Steps (a)–(d) of the process mapping a graph into a floor plan process. (e) shows the application of the hallway finding method to an entire floor plan and final geometry (f). Utilising generative design, a hypothetical school's classrooms and pedestrian flow were shaped to minimise walking distance and corridor use. The method employs methods like graph contraction and corridor expansion, inspired by ant colony algorithms for optimisation. (Carta et al., 2020)

Figure 17 School floorplan generative optimisation minimising corridor lengths (Carta et al., 2020)

The method we are using - either a simple algorithm or a complex neural network – even though it might be brilliant, as long as it cannot read our minds, it only reacts to the provided context. A good self-reflection is to overview our inputs and think how effective or relevant would be our very own solution solely relying on this information. When we provide the proper context, we have a coworker with insight, not an anonymous advisor. (Petřík, 2024)

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6 · DISCUSSION As the presented analysis focuses primarily on possible approaches and workflows, the discussion section extends the report into presenting more speculative and stronger opinion-based empirical data. Analysed information was not found contradictory in terms of both theoretical and empiri basis, except for the earlier argued ambiguous taxonomy of computational design.

6.1.1. Approaches to co-design nowadays Nowadays, parametric design is perceived pragmatically as a problem solver, an efficient modelling tool, not so much as a creative idealisation tool. The computational design is in studios utilised mainly in basic forms, quite usually for automation of routine work, as it often brings a competitive advantage with reasonable resource investment. Parametric scripts from experience end up on early-stage façade designs. Further development is usually manual, either for efficiency, limited abilities, or the uncertainty of implementation. (Kurilla and Prokop, 2024) Often single objective approach – have a stunning facades, but without context and skills needed to execute the task correctly, without systematic approach for further development, which is not very healthy in the long run. The self-reflection and overview are often missing before executing the process. (Petřík, 2024) An interesting characteristic of current computational design is that, in many cases, organic forms are easier to include in the process. As orthogonal forms need more individual segments, in contrast with continuous organic systems. Hence, subsequent modifications require much fewer rules and exceptions for organic architecture as the number of interacting surfaces is lower. So, by not abandoning organic forms, we essentially reinvent effective natural paths of shaping the design.

(Kurilla and Prokop, 2024) Nowadays, using generative AI means sacrificing control to some degree during the generation process, as understanding and fidelity to details are still quite limited. (Prokop, 2024)

6.1.2. Potentials in the future The future lies in collaboration, where the GPT model will react and deduce the context from our work - being an insightful advisor, assistant and colleague. Currently, we lack the critical assumptions of generative models about the information we have provided. They go along with anything, confidently generating a specific answer even for non-ideally formulated prompts. In a successful future, the GPTs should ask questions, raise concerns, and provide critical and relevant feedback. (Petřík, 2024) AI has the potential to return more creative fashion to work, helping to prototype and speed our process as copilot. AI network, which can act as an algorithmic architect, generate our scripts, prototype networks, break barriers and adapt solutions when switching to different algorithms or use cases. All of this allows individuals to focus on what is essential. It basically unlocks things that were locked in parametricism - but despite the advancements, we still need a deep understanding of the problem to have control over the details. (Kurilla and Prokop, 2024) Suppose we find a way to explain architectural principles and relations to neural networks, as we have successfully done with words in language. In that case, we might be able to train it to generate functional floor plans. I believe that some principles are shared; it is only a matter of adapting them to a non-linear and systematic process. (Kurilla, 2024)

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7 · CONCLUSION From a psychological perspective, the approach supports architects by offloading cognitive tasks and promoting a creative mindset centred around systematic exploration and experimentation.

Together, these philosophical and psychological dimensions contribute to a holistic understanding of how computational design shapes the conceptualisation and execution of architectural solutions.

Based on the combination of theoretical and empirical data, it was concluded that the potentials of computational design are beyond an effectivity-enhancing tool. When approached systematically, yet with an open and opportunistic mindset, these methods are becoming a powerful co-design tool that enhances the conceptual process. They allow us to undergo a more sophisticated and creative design loop, bound only by our skill of communicating intentions and context and picking the proper methods.

The ability to perceive these opportunities with an outlook and see beyond the results into the underlying principles allows one to explore the path to successful intent implementation. The area of computational design has robust applications in both existing architectural processes and emergent technologies of the future.

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8 · SOURCE LIST Articles: Caetano, I., Santos, L. and Leitão, A. (2020) ‘Computational design in architecture: Defining parametric, generative, and algorithmic design’, Frontiers of Architectural Research, 9(2), pp.

287–300. Available at: https://doi.org/10.1016/J.FOAR.2019.12.008.

Carbon, C.C. (2019) ‘Psychology of Design’, Design Science, 5, p. e26. Available at: https://doi.org/10.1017/DSJ.2019.25.

Carta, S. et al. (2020) ‘Self-organising floor plans in care homes’, Sustainability (Switzerland), 12(11). Available at: https://doi.org/10.3390/SU12114393.

Chaillou, S. (2019) AI + Architecture Towards a New Approach.

Chang, K.-H. (2015) ‘Multiobjective Optimization and Advanced Topics’, e-Design, pp. 1105– 1173. Available at: https://doi.org/10.1016/B978-0-12-382038-9.00019-3.

Creswell, A. et al. (2018) ‘Generative Adversarial Networks: An Overview’, IEEE Signal Processing Magazine, 35(1), pp. 53–65. Available at: https://doi.org/10.1109/MSP.2017.2765202.

Harding, J.E. and Shepherd, P. (2017) ‘Meta-Parametric Design’, Design Studies, 52, pp. 73–95.

Available at: https://doi.org/10.1016/J.DESTUD.2016.09.005.

Hohenecker, P. and Lukasiewicz, T. (2020) ‘Ontology Reasoning with Deep Neural Networks’, Journal of Artificial Intelligence Research, 68, pp. 503-540–503–540. Available at: https://doi.org/10.1613/JAIR.1.11661.

Kozma, R., Ilin, R. and Siegelmann, H.T. (2018) ‘Evolution of Abstraction Across Layers in Deep Learning Neural Networks’, Procedia Computer Science, 144, pp. 203–213. Available at: https://doi.org/10.1016/J.PROCS.2018.10.520.

Rutten, D. (2010) Evolutionary Principles applied to Problem Solving - Grasshopper, Grasshopper3d.com. Available at: https://www.grasshopper3d.com/profiles/blogs/evolutionaryprinciples (Accessed: 1 January 2024).

Wang, X., Song, Y. and Tang, P. (2020) ‘Generative urban design using shape grammar and block morphological analysis’, Frontiers of Architectural Research, 9(4), pp. 914–924. Available at: https://doi.org/10.1016/J.FOAR.2020.09.001.

Zheng, H. and Yuan, P.F. (2021) ‘A generative architectural and urban design method through artificial neural networks’, Building and Environment, 205, p. 108178. Available at: https://doi.org/10.1016/J.BUILDENV.2021.108178.

Books: Brawne, M. (2003) Architectural Thought: the design process and the expectant eye.

Goldsmith, N.S. and Ap, L. (2014) ‘Shape Finding or Form Finding?’

Webpages: Green, M. (2023) The Natural Building Blocks of Sustainable Architecture, TEDx talks. Available at: https://www.youtube.com/watch?v=KAiWdme6EEM (Accessed: 20 December 2023).

Harmon, B. (no date) An Introduction to Grasshopper. Available at: https://baharmon.github.io/basics (Accessed: 23 December 2023).

IBM (2023) What are Neural Networks? Available at: https://www.ibm.com/topics/neuralnetworks (Accessed: 1 January 2024).

Subjective interpretations of empirical data collected from interviews. Referenced paragraphs are not direct citations, rather extended interpretations of notes made during the interviews: Kurilla, L. and Prokop, Š. (2024), interview 4 January 2024, derived from interview keynotes Petřík, M. (2024), interview 3 January 2024, derived from interview keynotes Jurčík, P. (2023), interview 20 December 2023, derived from interview keynotes 25 · 26

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9 · FIGURE AND ILLUSTRATIONS LIST Figure 1 Analogy interpretation between human and computational design loop appended drawings from the personal archive for demonstration purposes. ................................................................. 10 Figure 2 Example of single-objective utilisation of parametric approach for architectural purposes(right). Visually, perhaps appealing form of steel welded truss inspired by voronoi (left), but the geometrical qualities of this principle are not utilised apart from aesthetic purposes.

Manufacturing of such element would be highly challenging (author's personal archive, Ostravar arena outline, 2022) ........................................................................................................................ 11 Figure 3 Parametric script with geometrical and numerical inputs generating structural frame for glazed fluid surfaces, Grasshopper script (top), resulting geometric results (bottom) (author's personal archive, academic studio project, 2023)........................................................................... 12 Figure 4 The connected model in Grasshopper, optimising spatial voronoi form, 3D representation (left), Grasshopper source script (middle), converging optimisation process graph (right), (APDesign Videos, youtube.com) .................................................................................................. 13 Figure 5 Antoni Gaudí, An upside-down force model of the Colònia Güell, Sagrada Família Museum (Canaan via Wikimedia Commons)................................................................................. 14 Figure 6 Generative pattern as the support structure for the AM production, (Ren & Galjaard, 2015) ........................................................................................................................................................ 14 Figure 7 Generatively optimised column-beam system (Green, 2023) .......................................... 14 Figure 8 Utilization of neural network for characteristic resemblance by NURBS body deformation „ (Zheng & Yuan, 2021), multiple figures from the original paper merged to capture a broader objective context. ............................................................................................................................ 15 Figure 9 Reference organisation modes according to the land (Wang et al., 2020) ....................... 16 Figure 10 Resulting generative adaptation, Buildings’ generative process and corresponding grammar structure, Generative urban design using shape grammar and block morphological analysis (Wang et al., 2020)............................................................................................................ 16 Figure 11 Multi-step pipeline to draw a floorplan using GANs (Chaillou, 2019) ......................... 17 Figure 12 Multi-objective optimisation with plugin Octopus for Grasshopper, a 3D graph of solution space (left), variant scores within optimised criteria (right) (Octopus plugin, food4rhino.com) ............................................................................................................................. 19 Figure 13 Plugin components corresponding to various phases and approaches (Octopus plugin, food4rhino.com) ............................................................................................................................. 19 Figure 14 Navigation through solution space with previews (Octopus plugin, food4rhino.com) . 19 Figure 15 Solution space of results from GAN processing, relations described through the form of clustering - Similarity Tree. Design branches computers according to Program Metrics (Chaillou, 2019) ............................................................................................................................................... 20 Figure 16 Deduced graph of existing relationships among rooms (left). Idealised adjacency matrix, reporting these connections (middle). A graph representation visually easily perceivable for a human (right) (Chaillou, 2019)....................................................................................................... 21 Figure 17 School floorplan generative optimisation minimising corridor lengths (Carta et al., 2020) ........................................................................................................................................................ 22

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