Generative Design in Architecture

GENERATIVE DESIGN IN ARCHITECTURE

SPECIAL STUDY

Bachelor of Architecture

Submitted By

HAFSA RAFI Under the Supervision of

AR. NAWAB AHMAD Department of Architecture Zakir Hussain College of Engineering and Technology Aligarh Muslim University Aligarh-202002 2020-2021

SELF DECLARATION CERTIFICATE

I, Hafsa Rafi, Department of Architecture, declare that this report entitled “Generative Design in Architecture” is my own bonafide work carried out by me under the supervision of Ar. Nawab Ahmad, at Aligarh Muslim University, Aligarh. I certify that this report has not been submitted by me for any other diploma/degree of this or any other university.

Date: 20.05.2021

Signature of Candidate HAFSA RAFI

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AR. NAWAB AHMAD (Supervisor)

External Examiner 1

External Examiner 2

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ACKNOWLEDGEMENT

First and foremost, praises and thanks to the God, the Almighty, for His showers of blessings throughout my report to complete the research successfully.

I would like to express my deep and sincere gratitude to my supervisor, Ar. Nawab Ahmad, Associate Professor, Department of Architecture, Aligarh Muslim University, for providing invaluable guidance throughout this research. His dynamism, vision, sincerity and motivation have deeply inspired me. He has taught me the methodology to carry out the research and to present the research works as clearly as possible. It was a great privilege and honor to work and study under his guidance.

I would like to express my gratitude to the Department of Architecture, the Chairperson and all the teachers for their constant support and guidance. I would like to thank all of my teachers for imparting quality education to us, even in these trying times.

I am extremely grateful to my parents and my brothers for their love, prayers, caring and sacrifices for educating and preparing me for my future.

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Table of Contents Table of Contents ...................................................................................... iv List of Figures ..........................................................................................vii List of Abbreviations ................................................................................. x Chapter 1: Introduction to Generative Design .............................................. 1 1.1 Introduction .......................................................................................... 2 1.2 Aim ...................................................................................................... 2 1.3 Objectives ............................................................................................ 2 1.4 Motivation and Methodology .............................................................. 3 1.5 Scope of work ...................................................................................... 5 Chapter 2: Developments in Generative Design in 1960s ............................. 6 2.1 Generative Design ................................................................................ 7 2.2 Milestones in the development of Generative Design ......................... 8 2.2.1. Generative Art in the 1950s 2.2.2. The application of CATIA 2.2.3. Celestino Soddu – Generative Design in Architecture Chapter 3: Early Software Developments and their Principles ................... 14 3.1 Generative Design Processes outlines by C. Soddu........................... 15 3.1.1. Designing a platform of architectural precedents 3.1.2. Progressive logics of transformation 3.1.3. Moving from Heuristics to meta-heuristics 3.1.4. Engaging subjectivity by giving an identity 3.2 Development of Basilica .................................................................... 17 3.3 Collaboration with Cellular Automata ............................................... 21 3.4 Model Development after 1988 ......................................................... 22 3.5 Development of Artificial DNA: Introduction to Argenia ................ 23 3.6 Learning from the development of Argenia....................................... 24 Chapter 4: Projects by Celestino Soddu using Generative Algorithms ...... 25 iv

Chapter 5: Evolution of Generative Design Understanding ........................ 28 5.1 Contribution of Parametricism and BIM ........................................... 29 5.1.1. Major contributions of parametricism 5.1.2. Major contributions of BIM 5.1.3. Conclusion 5.2 Contemporary understanding of Generative Design.......................... 31 Chapter 6: Application of Generative Design in Architecture..................... 35 6.1: Autodesk University Exhibit Hall..................................................... 36 6.1.1. Data collection for design requirements 6.1.2. Constraints in design space 6.1.3 Formulation Design Objectives 6.1.4 Design Space parametrization 6.1.5 Generating options based on primary goals 6.1.6 Evaluation of options-ensuring quality in quantity 6.1.7 Design Optimization 6.2 Autodesk MaRS Office, Toronto ....................................................... 49 6.3 Airbus Partition .................................................................................. 62 6.4. NASA: Satellite Antenna Design...................................................... 68 Chapter 7: Impact of Generative Design in Architecture and related fields 69 7.1. Benefits of Generative Design .......................................................... 70 7.2. Possible concerns .............................................................................. 71 Chapter 8: Summary and References ........................................................... 73 Chapter 1 .................................................................................................. 74 Chapter 2 .................................................................................................. 74 Chapter 3 .................................................................................................. 75 Chapter 4 .................................................................................................. 76 Chapter 5 .................................................................................................. 76 Chapter 6 .................................................................................................. 76 v

Chapter 7 .................................................................................................. 77 References ................................................................................................ 79

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List of Figures Figure 1: Proposed Methodology flowchart ...................................................... 4 Figure 2: Generative Design flowchart (Souza, 2020) ...................................... 7 Figure 3:Manfred Mohr’s Generative Art using Fotran (Manfred Mohr, n.d.)

(Mohr, 1977) ...................................................................................................... 8 Figure 4: Vera Molnar’s Digital Art (Molnar, Digital Art Museum, 2005) (Molnar) ............................................................................................................. 9 Figure 5: Milestones in the development of computational architecture (Souza, 2020) .................................................................................................................. 9 Figure 6: Diagram showing 3D geometry construction and visualization in CATIA. Non- uniform rational B splines used to construct more fluid forms.

(Souza, 2020) ................................................................................................... 10 Figure 7: Image used as reference for creating algorithm to generate Giotto’s description of medieval town (Soddu, Generative City Design: Aleatority and Urban Species, 2020) ....................................................................................... 18 Figure 8 (a-c): Different iteration of Giotto’s visualized medieval town generated using Basilica (Soddu, Generative City Design: Aleatority and Urban Species, 2020) ....................................................................................... 19 Figure 9: Paradigms generated by inclusion of Cellular Automata with Basilica (Soddu, 20 Years ARGENIA evolution, 2009) ................................. 21 Figure 10: Screen dumps of basilica in 1990 (Soddu, 20 Years ARGENIA evolution, 2009) ............................................................................................... 22 Figure 11: Chairs designed using Argenia (Soddu, 20 Years ARGENIA evolution, 2009) ............................................................................................... 22 Figure 12: Hong Kong City Identity in progress. Generative projects shown in the personal exhibition of Soddu at Visual Art Museum, 2002 (Soddu, 20 Years ARGENIA evolution, 2009) .................................................................. 26 Figure 13: Los Angeles: an office building, the broadcasting tower and IRTAL, shown at the personal exhibition of Soddu at Pacific Design Centre, L.A., and a new tower in “old” Chicago, 2002 (Soddu, 20 Years ARGENIA evolution, 2009) ............................................................................................... 26 Figure 14: Variations of the new Cultural center of World Bank in Washington D.C. (Soddu, 20 Years ARGENIA evolution, 2009)....................................... 26 vii

Figure 15: Futurism Museum in Milan, 2004 .................................................. 26 Figure 16: Shanghai Generative projects, a generated town environment belonging to the reconstruction of New York City artificial DNA and 3 tower “homage to Gaudi’”, using Basilica 2003 (Soddu, 20 Years ARGENIA evolution, 2009) ............................................................................................... 27 Figure 17: Data gathering, manipulation and optimization stages in time (Souza, 2020) ................................................................................................... 32 Figure 18:Simultaneous work-sharing between softwares. Live changes in Rhino(L) and Dynamo (R) (Erzetic, 2017) ...................................................... 33 Figure 19: AU booths and pavillons adjacence matrix. (Nagy & Villaggi, Generative Design for Architectural Space Planning, 2018) ........................... 36 Figure 20: AU Exhibit Hall Site Constraints (Nagy & Villaggi, Generative Design for Architectural Space Planning, 2018) ............................................. 37 Figure 21(a-g): Step by Step parametrization of design space (Wintour, 2021) .......................................................................................................................... 38 Figure 22: Placement of F&B and major events in the AU Exhibit hall ......... 40 Figure 23: (a-f): Parametrization representation of actual project (Nagy, Villaggi, Zhao, & Benjamin, 2017) ................................................................. 41 Figure 24:Buzz parameter considers the spatial distribution of high traffic areas in the plan (Nagy, Villaggi, Zhao, & Benjamin, 2017) .......................... 43 Figure 25:exposure parameter accounts for average foot traffic around each booth (Nagy, Villaggi, Zhao, & Benjamin, 2017) ........................................... 44 Figure 26:x and y axis represent buzz values; z axis shows their corresponding exposure models (Nagy, Villaggi, Zhao, & Benjamin, 2017) ......................... 46 Figure 27:Three selected designs, refined manually to accommodate suggestions from stakeholders ......................................................................... 48 Figure 28:The finalized plan of the AU Exhibit Hall 2017 ............................. 48 Figure 29:Defining the boundaries of design space (The Living, 2018) ......... 49 Figure 30:Identification and classification of design space requirements (The Living, 2018).................................................................................................... 50 Figure 31:Goal formulation on the basis of parameters (The Living, 2018) ... 50 Figure 32:Prioritization of objectives. Setting up base objectives for running simulations. (The Living, 2018)....................................................................... 51 Figure 33:Flowchart of Generative Design Stages (The Living, 2018)........... 51 viii

Figure 34:Defining design space boundary and existing columns (The Living, 2018) ................................................................................................................ 52 Figure 35:Fixed Zones defined in design space (The Living, 2018) ............... 52 Figure 36:Division of areas into sections (The Living, 2018) ......................... 53 Figure 37:Manipulation in seed points and neighborhood boundaries (The Living, 2018).................................................................................................... 53 Figure 38:Placement of components (The Living, 2018) ................................ 54 Figure 39:Placement of components (The Living, 2018) ................................ 54 Figure 40:Adjacency preference and circulation mapping in design space (The Living, 2018).................................................................................................... 55 Figure 41:Workstyle preference (The Living, 2018) ....................................... 55 Figure 42:Buzz mapping for activation of shared spaces through individual and team movement (The Living, 2018).......................................................... 56 Figure 43:maximizing productivity by reducing audio and visual distractions (The Living, 2018) ........................................................................................... 56 Figure 44:Daylight mapping to provide natural light in the working space (The Living, 2018).................................................................................................... 57 Figure 45:Daylight mapping to assess heat gains (The Living, 2018) ............ 57 Figure 46:Exterior opening and context studied for providing positive outside views (The Living, 2018) ................................................................................. 58 Figure 47:List of options presented to the stakeholders (The Living, 2018) ... 58 Figure 48:Placement of Specialty spaces (The Living, 2018) ......................... 59 Figure 49:Placement of meeting spaces (green) and specialty spaces (white) (The Living, 2018) ........................................................................................... 59 Figure 50:Administrative fixed zones (orange) (The Living, 2018)................ 60 Figure 51:Location of facilities(red) (The Living, 2018) ................................ 60 Figure 52:Final Floor plan of MaRS Office (The Living, 2018) ..................... 61 Figure 53:Initial Sketches by Bastian Schaefer for the partition (Autodesk , n.d.) .................................................................................................................. 62 Figure 54:Iterations of growh patterns generated by the software (Autodesk , n.d.) .................................................................................................................. 63 Figure 55: Testing structural integrity of various options generated. (Autodesk , n.d.) ................................................................................................................ 63 Figure 56:Bionic partition finalized design (Autodesk , n.d.) ......................... 64 ix

Figure 57:Softwares used to create the bionic partition (Autodesk , n.d.)....... 64 Figure 58:Network 1 for slime mold segments (Autodesk , n.d.).................... 65 Figure 59:Network 2 for slime mold segments (Autodesk , n.d.).................... 65 Figure 60:Network 3 for slime mold segments (Autodesk , n.d.).................... 66 Figure 61:Network 4 for slime mold segments (Autodesk , n.d.).................... 66 Figure 62:Network 5 for slime mold segments (Autodesk , n.d.).................... 67 Figure 63:Mammal bones representation in the lattice (Autodesk , n.d.) ........ 67 Figure 64:Antenna designed in 1960s (L); Antenna design by GD (R) (Kowalski , 2016)............................................................................................. 68 Figure 65:Accelerating growth graph (Allen , 2016)....................................... 71 Figure 1: Tentative Workflow of Generative design approach derived on the basis of examples…………………………………………………………….77

List of Abbreviations GA: Generative Algorithm GD: Generative Design

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CHAPTER 1: Introduction to Generative Design in Architecture

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CHAPTER 1: INTRODUCTION

1.1 Introduction Architecture being an interdisciplinary subject requires expertise in various fields. Anything more challenging than having expertise in numerous fields is having the ability to prioritize parameters for reaching an outcome. To decide, usually an iterative approach is taken and all variations are evaluated to find an

outcome which is close to the desired results. However, this iterative process has become tedious due to the presence of numerous influencing factors. These factors are accompanied by tremendous amounts of digitally analyzed data.

Hence, after parametric and BIM software, the field is looking forward towards incorporating artificial intelligence in the designing process. A computer can’t be given a designer’s creativity or his intuition but it surely can aid the process of designing by analyzing data and interpreting it into a series of possible spatial configurations. With the vision to automate the designing process, the concept of generative design was formulated.

1.2 Aim The aim of this study is to understand the logical reasoning used for formulating generative algorithms and study the implementation of generative design approach in architectural practice.

1.3 Objectives •

To study the underlying logic that formulates iterations for a design problem.

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To understand the pre-requisites required to run generative algorithm simulations.

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To enlist steps in generative design process which formulates preferred design iterations.

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1.4 Motivation and Methodology The story about Frank Gehry’s use of CATIA while defining construction parameters for fish shaped pavilion for ’92 Olympics in Barcelona, was what inspired the search for related topics. The pursuit to initiate generative design approach has been in play since the 1950s when architects tried to establish a mathematical logic to justify the architect’s intuitive approach. Even though it failed at that time but it worked in the long run giving rise to parametric design software.

Considering we have the platform for establishing iterative design softwares, this study aims to collect secondary data sources to identify the factors that led to the development of the field.

Information about the development and research on iterative design softwares has been collected from reliable sources such as Autodesk workshops, Autodesk experts’ workshops, MIT research programs and other PG dissertations. Apart

from this, the ideas and the philosophy were studied from Celestino Soddu’s initial ideas about generative art and architecture.

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Figure 2: Proposed Methodology flowchart

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1.5 Scope of work SCOPE: This study will investigate the logical reasoning used to develop the generative design approach. It will also analyze the workflow- the input parameters, the processing algorithms, design evaluation and optimization in the generative design approach. Examples will be used to highlight the advantages and address the challenges faced by designers during the implementation of generative design approach to their design problems. The process and the software development will only be explored with the intent of judging its constraints and its opportunities.

LIMITATIONS: The study will not discuss the efficiency of the software interface, or the softwares used to achieve generative design goals or comment on the software commands or generative engines used. Technical specifications of softwares and their working is not within the scope of this study as of now.

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CHAPTER 2: Developments in Generative Design in 1960s

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CHAPTER 2: DEVELOPMENTS IN GENERATIVE DESIGN IN 1960s

2.1 Generative Design Generative design is based on a concept of exhaustive exploration of design variations (Souza, 2020). It is a design proposition that interprets quantifiable parameters into conceptual design forms which meet the constraints set by the

designer. It produces many iterations based on the parameters defined and allows the designer to refine his choices and decisions on the basis of its output.

Figure 3: Generative Design flowchart (Souza, 2020)

“A type of evolutionary process that is able to generate a sequence of results where each result is different, but recognizable as belonging to a

species.” (Bentley & Corne , 2002)

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The concept of algorithm driven designing is not particularly recent. The field is being explored and is under development since 1960s.

2.2 Milestones in the development of Generative Design 2.2.1 Generative Art in the 1950s Before generative design was applied to the architectural practice, many digital artists successfully incorporated it. Among them was Vera Molna (Molnar)r, a Hungarian digital artist. In early 1960s she used Fortran. It a programming language used to generate images according to theme and generated automated variation in her works. Another digital artist, Manfred Mohr, generated variations of 3D geometry and created what may be called as ‘algorithmic art’ in 1970s. (Knemeyer & Follett, 2020) Later, an article in 1972 titled “Shape Grammars and Generative Specification of Painting” and Sculpture” presented a manual encoding of design systems.

The authors, George Stiny and James Gips, defined a production system for generating shapes much like options generated by generative design today.

(Knemeyer & Follett, 2020)

Figure 4:Manfred Mohr’s Generative Art using Fotran (Manfred Mohr, n.d.) (Mohr, 1977)

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Figure 5: Vera Molnar’s Digital Art (Molnar, Digital Art Museum, 2005) (Molnar)

Figure 6: Milestones in the development of computational architecture (Souza, 2020)

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2.2.2 The application of CATIA - 3D representation of fluid forms First application of automation in architecture was witnessed in 1982, when Frank Gehry used CATIA to estimate the shape and execute the construction of his fish shaped pavilion for the 1992 Olympics in Barcelona, Spain. When the contractors failed to translate and execute his vision, Jim Glyph in Gehry Technologies turned to CATIA, a C++ program originally developed for aerospace industry. (Korody, 2015)

The software used 3D surface algorithms and Bezier vectors to accurately define the geometry. Instead of 2D construction drawings, the office sent 3D drawings on site for better comprehension. (Korody, 2015) “… We found that the more precise the information, the more it could be demystified and reduced to the ordering of materials of a certain shape and almost the ability to the contractor to paint by numbers. It gave the contractors security in the bid and presented inordinate premiums.” (Van Bruggen, 1997)

Subdivision

Polygon Mesh

Non - uniform rational B splines

Figure 7: Diagram showing 3D geometry construction and visualization in CATIA. Non-

uniform rational B splines used to construct more fluid forms. (Souza, 2020)

Gehry’s intervention in the 3D architecture softwares, transformed the practice.

Earlier, the softwares were limited to generation of linear forms and execution was dependent on 2D construction drawings and on the understanding of the contractor.

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With the inclusion of CATIA, along with other softwares developed by Gehry Technologies, fluid forms could be accurately defined and executed with the help of 3D drawings.

2.2.3 Celestino Soddu – Generative Design in Architecture Generative Design owes a great deal of its success to Celestino Soddu. He is an architect and professor at Politecnico di Milano university in Italy. His research on the topic proved to be a breakthrough for development of generative

softwares.

Since 1979, his research has been about the dynamic evolution of the artificial systems complexity and image, designing sequences of original software to emulate and control architectural, environmental and industrial design processes and to improve the manufacturing industrial processes. (Celestino Soddu Resume, 2007)

The research done by Celestino Soddu is a documentation of his approach to develop his Generative Design software called Argenia. It deals with questioning the factors that influence the design process. More importantly, it aims to explore the starting point for any algorithms to yield desired results.

He theorized that for any generative system to produce variations, certain prerequisites are required. According to him, generative design is like seasoning an abstract idea in a set of conditions. These conditions are a careful construct of overlays which define an artificial environment and its parameters. The concept is much like a seed growing and adapting itself to its environment.

(Soddu, Endless Interpretations, infinite in the mirror, 2007) In his research titled, ‘Endless Interpretations, Infinite in the Mirror’, Soddu stated the fundamental parameters that should be defined for setting a platform for automated designing.

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2.3.3.1. The Starting ‘Point’ Every project in architectural design begins from something-a ‘point’ on a blank page. Usually referred as ‘concept’ in design process, the initial 11

idea is vague and comes from the intuitive sense of the designer. The challenge is to define this vague parameter in the software so that it can build upon the initial idea. Defining a starting point involves translation of an abstract idea into computer algorithms which define the spatial configuration.

At the end of this stage, we have a well-defined objective or intent of design, expressed in mathematical terms. This expression defines a

‘starting point’ in a three- dimensional space.

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2.3.3.2. The Logical Structure After defining the starting point, other factors are taken into account and their mathematical expressions are generated. These other factors might be designer’s preferences, may be based on past precedents or take into account the wide-ranging functions the building is intended for.

This is set of algorithms that will determine how the idea will grow. The overlay of these algorithms, their interaction with each other and the starting point will create an artificial environment in which the idea will grow and transform.

At the end of this stage, we will have a random network of algorithms, all related to each other forming a complex overlay. The resultant of this complex matrix will be responsible for the transformation of model and generate viable design options for consideration.

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2.3.3.3. Evaluate and evolve results This stage of model development allows the user to cross check the options created by the software to the required objectives. If the variations fail to meet the criteria, the parameters are redefined and options are evaluated again. It is a build-measure-learn process.

This stage is the most important of all the other stages as it allows the designer to witness the transformations in the model as the changes in

parameters are made. This iterative process enables the designer to explore.

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2.3.3.4. Context and project While the previous three stages defined the form of a model in an ideal space which is not affected by surrounding factors. However, no building exists in solitude. Therefore, the evolution of the design depends on two factors- The concept or initial idea and the context (existing surrounding conditions).

The initial idea encapsulates all unpredictable design transformations.

The unpredictability ensures that the form has a distinctive identity. All variations produced by one initial idea will be different in many ways but will exhibit a familiarity due to its unique concept or starting point (Soddu, The Design of Morphogenesis. An experimental research about logical procedures in design process, 1994) The other aspect of evolution involves considering existing surroundings. These are unpredictable but identifiable paramters

(Soddu, The Design of Morphogenesis. An experimental research about logical procedures in design process, 1994). They provide a sense of belonging to the project, making it blend into its surroundings. Apart from respecting cultural richness, the surrounding conditions modifies the form of the model to maximise environmental benefits through passive measures built into form and planning.

“This stage fulfils the final criteria of modifying the form of the building/project according to cultural and environmental context” (Soddu, The Design of Morphogenesis. An experimental research about logical procedures in design process, 1994).

With these four logical procedures, Celestino Soddu initiated an experimental AI software capable of incorporating subjectivity in architectural design projects (Soddu, The Design of Morphogenesis. An experimental research about logical procedures in design process, 1994). “This software could exhault its own creativity by interpretation of existing events as dynamic systems, managing their evolution with own rules of transformation”. (Soddu, Endless Interpretations, infinite in the mirror, 2007) 13

CHAPTER 3: Early Software Developments and Their Underlying Principles

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CHAPTER 3: EARLY SOFTWARE DEVELOPMENTS AND THEIR PRINCIPLES The main intention for designing generative design software was to provide a reasonable explanation for the set of choices made while designing and justify the final outcome through comparison with other possible variations.

Secondly, it could enable the designer to explore unexpected results engendered by his initial idea. Thus, leaving room for unconventional, unpredictable

solutions to be discovered.

Celestino Soddu developed his “experimental representation of complex, nonlinear systems to manage multiple bifurcations and variations in a morphological sense” (Soddu, 20 Years ARGENIA evolution, 2009).

In 1979, he developed a software which used 2D reverse perspective to generate 3D models. Along with this, his experimental research on representation made him suggest the use of fractal geometry for creating a natural environment.

(Soddu, 20 Years ARGENIA evolution, 2009)

3.1 Generative Design Processes outlines by C. Soddu Shortly after this, Soddu developed his vision for generative design software.

Objectives of his vision for generative architecture were devised by studying the challenges he faced with developing algorithms. The following were the challenges faced:

3.1.1. Designing a platform of architectural precedents A database or algorithms was required. The program had to be taught about the existing architectural styles. Innovations can only be built by permutation and combination of existing algorithms. Knowledge of past precedents and their application in combination with the abstract idea could generate unpredictable design solutions.

It is like applying Turing’s experiment to architectural design. In other words, giving the ability to think to a computer. Only with the ability to decide can a computer algorithm account for subjectivity in the design process.

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1.1.2. Designing an engine which could produce progressive logics of transformations (Soddu, 20 Years ARGENIA evolution, 2009) For creating a complex overlay, the parameters and their subsequent choices are required to function in an orderly manner. Stratification is done of these choices. This helps to classify, prioritize and merge

parameters to create a hierarchy.

After stratification, existing systems are interconnected to form a network. Each algorithm/design choice is like a neuron and we are looking at developing a network which functions like a brain. Variations in this network and how they interacted with each other would give rise to a sequence of algorithms developing more algorithms. Thus, a progressive network of transformations.

The final step would be to record the results of these transformations and let them operate as driving factors of the design process.

1.1.3. Moving from Heuristics to meta-heuristics Heuristics refer to a set of solutions which are formulated by their success in past precedents. These are thumb rules which deal with a generic set of problems. Solutions generated by thumb rules are faster but may not always be the optimum solution in every case. Results generated by following heuristic approach are predictable and inefficient because they are not tailor- made for the set of problem in consideration (Nagy & Villaggi, Generative Design for Architectural Space Planning, 2018) On the other hand, ‘metaheuristics’ refer to “a set of optimization techniques that for a given complex problem, can find a set of overall best solutions by iteratively sampling solutions and using performance criteria to generate better and better outcomes” (Nagy & Villaggi,

Generative Design for Architectural Space Planning, 2018)

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The main objective of introducing metaheuristics was to move past the traditionally accepted layouts. For achieving this, subjectivity had to be introduced in the generative algorithms.

“Design processes which didn’t deal with a set of complexes overlapping challenges were usually axiomatic, linear and did not yield innovative results” (Soddu, 20 Years ARGENIA evolution, 2009) To achieve subjectivity, complexity had to be added and it was done by producing an array of complex algorithms, progressively producing more algorithms. Basically, it was the algorithm teaching itself to make more algorithms before narrowing it down to reach desired results.

1.1.4. Engaging subjectivity by giving an identity To make a project seem native of its surroundings, the last set of subjectivity introduced into the program was engaged by giving it an identity. Identity might be an artificial conscience developed of the

natural environment or the ingenuity of the original idea. Identity would be the essence the designer wants to convey through its design.

3.2 Development of Basilica After seven years of experimental research on the above-mentioned objectives, Soddu developed Basilica in 1986. This is a predecessor of Argenia which concentrated on development of urban surroundings for medieval towns and cities. These urban environments were usually translations of work by Simone Martini and Giotto di Bondone (Soddu, 20 Years ARGENIA evolution, 2009).

Simone Martini is responsible for the development of Gothic Style while Giotto di Bondone was an artist and architect in Florence. Their descriptions and paintings have been used to generate ideal medieval towns. Multiple variations were generated on the basis of their vision of medieval towns.

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Figure 8: Image used as reference for creating algorithm to generate Giotto’s description of medieval town (Soddu, Generative City Design: Aleatority and Urban Species, 2020)

(a)

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(b)

(c) Figure 9 (a-c): Different iteration of Giotto’s visualized medieval town generated using Basilica (Soddu, Generative City Design: Aleatority and Urban Species, 2020)

The engine, basilica, could generate multiple variants of subjective transformations, modifying itself in second and third dimension. However, it was limited by the technology of that time. Any changes made in the program yielded an output overnight. To make further changes, another night was required for the program to exhaust its set of options.

“The main difficulty of these first experimentations of the middle of ‘80s was the time due to verify the system. Because the screens with green

or yellow phosphorus were at low resolution, the only possibility was to directly trace a representation through the pen plotter. I launched in the evenings the program and the subsequent mornings I got up for seeing 19

the result. Updated the program I had to wait a lot for verifying it again.”

(Soddu, 20 Years ARGENIA evolution, 2009) However, with practice handling transformations became easier.

“Basilica worked on three foundation principles: 1. Identifying organizational paradigms of architecture able to define events, relationships and interferences

2. Tracing initial events that define, in first approximation, the dimensions and the orientation.

3. Managing ranges of geometric transformations, each one able to increase one of the functional / aesthetical / symbolic aspects and to push the events toward my architectural Vision.”

(Soddu, 20 Years ARGENIA evolution, 2009)

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3.3 Collaboration with Cellular Automata Basilica worked with transformations which worked simultaneously in the metamorphosis of single events and that of the whole project. This led to redundancy in these logics. The program could not determine when and how these logics of transformations should be activated or what to choose in a particular situation (Soddu, 20 Years ARGENIA evolution, 2009). Thus, Cellular Automata was incorporated in the code of the program. This generated topologic models which adhered to the identity of the project. This determined which logics were more probable to be reflected in the design by analyzing its starting point in the code.

The starting point ensured that the results generated could be recognized as the belonging to the same species but were unpredictable due to nonlinear complex systems which led to its discovery. (Colabella, 2006)

Figure 10: Paradigms generated by inclusion of Cellular Automata with Basilica (Soddu, 20 Years ARGENIA evolution, 2009)

“Basilica I used specific geometric parametric algorithms, algorithms managing the transformation of event's figure by moving from a dimension to another, Cellular Automata and parallel progressions of transformations of single events that dynamically interact with others, as flocking of birds, and structures of repetition of the same algorithm applied to the same event, as fractal approach. But none of these methods is primary. The peculiarity of my approach is “how” I use them all together. It is the expression of how it's possible to effort single, unexpected and unpredictable requests with the aim to fit my Vision of Architecture. The main question is not only the tools but the right aim.”

(Soddu & Colabella, Generative Art and Architecture, n.d.)

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3.4 Model Development after 1988 The development in computer technology enabled Soddu to record his iterations and publish them together in his book Città Aleatorie, which is a compilation of urban environments generated by Basilica and its successor Argenia.

Figure 11: Screen dumps of basilica in 1990 (Soddu, 20 Years ARGENIA evolution, 2009)

Breakthrough however was made by implementing and evolving Argenia by applying it to the interdisciplinary filed of Generative Art. Basilica was developed and used for industrial design, product design and to create art. The main aim was to make Basilica an open software which was user friendly and reached out not only to designers but to a larger demographic. The interface was developed to ensure that the user could efficiently apply it with primitive knowledge of algorithms.

Figure 12: Chairs designed using Argenia (Soddu, 20 Years ARGENIA evolution, 2009)

“In 2001, Soddu developed and experimented the feasibility of a direct interaction between my generative software Argenia and rapid

prototyping devices, and therefore with industrial devices at numerical control. He successfully managed the possibility to directly produce unique objects by using these devices. Argenia opens this possibility by 22

generating in real time unique STL files usable for producing a sequence of unique objects.” (Soddu, 20 Years ARGENIA evolution, 2009)

3.5 Development of Artificial DNA: Introduction to Argenia The algorithm, over the years of 20 years of experimental research by Celestino Soddu and Enrica Colabella, developed into a DNA. It was found during research that minimal changes in a set of preexisting algorithms could lead to

the transformations bearing a unique identity. Thus, a complex overlay of 20 years’ worth of algorithms, i.e., the DNA was compiled and made capable of customization at the behest of the user.

Argenia became an open-source software which catered to the needs of all designers, architects and artists. “Domus Argenia has the aim to develop exchange among different creativeness and different disciplines in a cultural approach focusing on Identities, the subjective creativeness and different cultural heritages.” (Soddu, 20 Years ARGENIA evolution, 2009) For designing with Argenia, Soddu outlined six fundamentals for development and modification of the base algorithm.

1. Identification of initial Idea and comprehension of the ‘vision’.

2. Identification of design moments: Determining how the spatial transformation or molding of form would take place.

3. Introducing subjectivity in the algorithms by adding preferences and

elaborating design vision/concept. Thus, developing an identity of the project.

4. Setting up a hierarchy in the algorithms, creating a complex array of progressively transforming algorithms.

5. Evaluating the generative process on the basis of the outcomes produced and modifying paradigms.

6. Exploring representations of the idea produced by the engine and

optimizing results for the desired result with maximum efficiency.

(Soddu, Teaching Text : Generative Design, n.d.)

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3.6 Learning from the development of Argenia The field of generative design established some postulates on the basis of the research done for the development of generative software, Argenia. The prerequisites for the development of any project in a generative environment became clear. Subjectivity and context could be defined in computational terms and it could be controlled how and when they would affect the result of the project.

Even though the intangibles were well defined to the software and made clear to the user, generative design failed to be incorporated as a design method in architectural practice.

The approach relied on giving an architectural understanding to the engine. This was done by programming the learning derived from past precedents. However, at that time digital documentation had just begun and the approach failed due to the lack of a digital database.

Second major cause of failure was the processing speed of computers. Any change made in the code was witnessed in the result after hours or days.

Automated design software has a higher probability to be incorporated and efficiently used in architectural practice today. This is mainly due to the development of BIM (Building Information Modelling) and parametric softwares. The development of these two programs in the field has given an insight into Celestino Soddu’s original principles. Gradually over the years we

have achieved the prerequisite database required for generative algorithms to optimize workflow.

The next chapter discusses the collaboration of parametricism and Building Information Modelling in advancement of generative design software.

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CHAPTER 4: Projects by Celestino Soddu on Basilica and Argenia using

Generative Algorithms

25

4.1. Generation of urban cities using GD

Figure 13: Hong Kong City Identity in progress. Generative projects shown in the personal exhibition of Soddu at Visual Art Museum, 2002 (Soddu, 20 Years ARGENIA evolution, 2009)

Figure 14: Los Angeles: an office building, the broadcasting tower and IRTAL, shown at the personal exhibition of Soddu at Pacific Design Centre, L.A., and a new tower in “old” Chicago, 2002 (Soddu, 20 Years ARGENIA evolution, 2009)

Figure 15: Variations of the new Cultural center of World Bank in Washington D.C. (Soddu, 20 Years ARGENIA evolution, 2009)

Figure 16: Futurism Museum in Milan, 2004

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Figure 17: Shanghai Generative projects, a generated town environment belonging to the reconstruction of New York City artificial DNA and 3 tower “homage to Gaudi’”, using Basilica 2003 (Soddu, 20 Years ARGENIA evolution, 2009)

27

CHAPTER 5: Evolution of Generative Design Understanding

28

CHAPTER 5: EVOLUTION OF GENERATIVE DESIGN UNDERSTANDING

5.1 Contribution of Parametricism and BIM The development of parametricism has incorporated algorithms for exploring complex spatial configurations in buildings. The AEC industry has evolved past 2D drafting implements. Design forms are being dealt with softwares from initiation till execution on site. The buildings are not limited to linear forms anymore.

5.1.1. Major contributions of parametricism Since parametricism defines spatial relationships through algorithms, design forms are greatly flexible. Any changes in the root algorithm or the progressive algorithms are reflected directly on the design form.

Designs achieved/ formulated through parametric softwares are capable of

•

accommodating modifications seamlessly,

•

Integrating large number of influencing factors/parameters

•

Producing faster, iterative results

•

Optimizing designs to yield the desired output which proves to be environmentally friendly, cost effective and innovative.

Parametric geometries achieve “complexity from ordered simplicity”. A basic geometry develops into a complex pattern with simple transformationsoverlapping, rotation, repetition (LS, n.d.)

Looking back on Soddu’s principles, the above-mentioned principles clearly outline the prerequisites mentioned by him in his research paper- Endless Interpretations- Infinite in the mirror and in 20 Years of Argenia.

Parametric Softwares are capable of defining a starting point in 3D space, formulating progressive algorithms functioning within constraints, can transform geometries and most importantly, they are able to analyze and

optimize designs according to environmental constraints. All of these were foundational principles for Celestino Soddu’s original requirements which led him to develop the first generative design software-Argenia.

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5.1.2. Major contributions of BIM Building Information Modelling or BIM serves as a communication tool between different design formulation and execution stages. Primarily, this has been instrumental in demystifying/clarifying the architect’s vision to contractors, engineers, project managers and the client.

BIM involves 3D representation of buildings with detailed information about material specifications, quantities and estimated time for completion of project.

It takes into account factors like- daylight exposure, natural ventilation through air currents and other factors influencing microclimate of site. The context is incorporated from the beginning into the design process. The software optimizes the building’s carbon footprint, minimizes heat gains, optimizes and estimates the material quantities and project cost. Execution of non-linear forms and determination of their structural integrity has been improved by the use of BIM in 3D modelling of structures.

5.1.3. Conclusion The developments in BIM led to the formulation of a digital library/ database which could be accessed for developing the computer’s understanding of architecture. The context and other intangibles could be expressed in terms of algorithms which could define the position and function of geometries in a three-dimensional space.

Moreover, parametricism enabled progressive development of geometries from simple concepts. It allows seamless integration of modifications in real time.

Incorporating generative algorithms and introducing complexity and subjectivity in the array of parametric softwares is comparatively easier.

Generative design is being developed after the development of BIM and Parametricism, carrying advantages of both. This coupled with algorithm advancements and development in simulations, have set a higher probability of Generative design being successful in architecture.

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5.2 Contemporary understanding of Generative Design In 2016 Autodesk University lectures, Bill Allen describes that the development of Building Information Modelling can be divided into three major time periods.

Until 2015, architecture was in a ‘Data Gathering Stage’. This involved static modelling of buildings and input of information manually. Any iterations introduced had to be remodeled manually and requirements adjusted accordingly in spreadsheets. This was the beginning of 3D representation of buildings. (Allen , 2016) We have been in the ‘Data Manipulation Stage’. This majorly includes development of algorithmic modelling and parametricism. Iterations and changes made in the model do not require manual remodeling but require change in numerical values in base algorithms. Grasshopper and dynamo are plugins in Rhino and Revit respectively that integrate BIM and algorithmic modeling (Allen , 2016) The industry is looking towards ‘Data Otimisation Stage’. Modelling softwares are now developing to generate design options on the basis of desired parameters and constraints. The aim is to look for the most efficient solution for a design problem. Autodesk has introduced Dreamcatcher Project which incorporates generative design in architecture. The application of generative design has improved our understanding of structural integrity of structures, helped us improved sustainability measures and improved habitability of buildings (Allen , 2016) These understanding of time periods help us map the step-by-step development of softwares.

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Figure 18: Data gathering, manipulation and optimization stages in time (Souza, 2020)

In an article defining the role of generative design and explaining its progress and its benefits in architectural softwares, (Autodesk, n.d.) has outlined the basic steps in the process of formulation of a design form through generative design. The workflow includes the following stages: 1. Generate Algorithms are defined and constraints are set to make the program ready to generate viable options.

2. Analyze Design forms and plans generated are cross referenced with their desired objectives.

3. Rank Options are narrowed down and ranked on the basis of user preference.

4. Evolve Algorithms are refined to achieve more optimization in the most

preferred alternatives.

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5. Explore Stakeholders and designers are open to adopt unconventional or innovative solutions due to the comparative analysis presented to them.

Thus, exploring innovative solutions to challenging problems.

6. Integrate After the selection of most appropriate option, the model can be interpolated and coordinated in different softwares for execution of the project (Autodesk, n.d.)

Figure 19:Simultaneous work-sharing between softwares. Live changes in Rhino(L) and Dynamo (R) (Erzetic, 2017)

“GD is constituted by three main components: 1) a generative model that can describe a wide design space of possible solutions; 2) an evaluative component that comprises the specified design goals; 3) a metaheuristic search algorithm, in this case a GA, that can navigate the design space and generate better and better design solution” (Nagy & Villaggi,

Generative Design for Architectural Space Planning, 2018) For simplifying workflow, the above mentioned six processes are classified as Pre-GD, GD and Post GD. Pre GD consist of acquiring data and setting 33

constraints for the project. Every piece of information gathered for development of algorithms is accounted for in this stage. Then GD constitutes the formulation of options by the software by processing the input parameters. Post GD deals with evaluation of options and refining algorithms to arrive at the most optimum solution. These steps will be well understood through case studies discussed in the next chapter.

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CHAPTER 6: Application of Generative Design in Architecture and related fields

35

CHAPTER 6: APPLICATION OF GENERATIVE DESIGN

6.1: Autodesk University Exhibit Hall Designed by: The Living An Autodesk Studio which applies research to architectural practice.

This project is chosen because its documentation available online was published by its designers and it clearly highlights their workflow. Therefore, this project will prove to be instrumental in studying the practical application of Generative Design to design problems.

PRE GD-OPERATIONS 6.1.1 Data collection for design requirements Design requirements of the pavilion, adjacency preferences and preferred locations of different stalls is gathered and a matrix defining these relationships was prepared by the designers for better understanding.

Figure 20: AU booths and pavillons adjacence matrix. (Nagy & Villaggi, Generative Design for Architectural Space Planning, 2018)

36

6.1.2 Classification and identification of constraints in design space 1. Design Constraints: Placement of all booths with respect to access points in the design space and the main hall.

2. Pre-Existing Constraints: Defining design space boundaries, columns, egress areas and restrooms.

3. Access Constraints: Fixed access points in the design space.

(Nagy & Villaggi, Generative Design for Architectural

Space Planning, 2018)

Figure 21: AU Exhibit Hall Site Constraints (Nagy & Villaggi, Generative Design for Architectural Space Planning, 2018)

6.1.3 Formulation Design Objectives With discussion, the designers and stake holders came to the conclusion that the design required mapping and distribution of high activity zones to manage traffic in the pavilion. Secondly, the design had to evaluate

the required proximity of booths to high activity zones (Nagy & Villaggi, Generative Design for Architectural Space Planning, 2018)

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6.1.4 Design Space parametrization After setting constraints and formulating goals for the design space, parametricism of the design space is required.

a.

b.

c.

d

e.

f.

g.

Figure 22(a-g): Step by Step parametrization of design space (Wintour, 2021)

38

For understanding, the shape of Exhibition Hall is assumed to be a rectangle as shown in Fig 8: (a-g). The design proceeded as indicated in the steps below. a. Defining the boundary of the exhibition hall. Thus, setting extents of the project from t=0 to t=1 (where it is the name of parameter). The boundaries are assigned as reference parameters for defining positions of other paramters with respect to it. b. Using existing access points (entry and exit) to determine the placement of the main circulation route in the exhibition hall. The major avenue is subdivided into minor avenues as required. c. Parametric range is set for the points of minor avenue to move along the boundary, to find the best arrangement and subdivision of hall.

The range for set of points belonging to the first partition is from t=0.1 to t=0.45. Similarly for second minor avenue the range is from t=0.55 to t=0.9 (Wintour, 2021) The information in design space is guided by two types of parameters.

Steps a and b above are carried out using the initial three paramters stated in the beginning (Fig 3). The second set of 22 paramters are used for the subdivision and placement of the major expo programs along the circulation routes (Nagy, Villaggi, Zhao, & Benjamin, 2017) d. After finding the most optimum placement for minor avenues, the floor plan is subdivided further into sections, A, B, D, E, F. e. These macro regions are divided into micro regions on the basis of the following principles (as outlined in (Nagy, Villaggi, Zhao, & Benjamin, 2017): “1. Identify macro-regions that share at least one edge with the exhibit hall boundary.

2. For each of these macro-regions identify the longest edge that is shared with the exhibit hall boundary and locate the midpoint of the edge.

39

3. Draw a secondary avenue line perpendicular from this midpoint to the opposite edge of the macro-region.”

(Nagy, Villaggi, Zhao, & Benjamin, 2017) f. Fixed grids of 40’ x 20’ are used to further subdivide the micro regions in cells for space allocation of booths in the pavilion.

The division of space was optimized using algorithms. On the proposed space, allocation of spaces had to be done to meet the goals formulated in the pre- GD stage.

Figure 23: Placement of F&B and major events in the Au Exhibit hall

•

Allocation of F&B zones was done first to ensure that it was evenly distributed and easily accessible to the visitors that pass by and to the ones attending the exhibition. “This strategy distributes the F&B programs around the floor plan, avoids dead ends, and minimizes congestion while creating attractions that draw people further into the space” (Nagy, Villaggi, Zhao, & Benjamin, 2017)

•

11 Major program locations were pinpointed in the space. These are known as “seed points”.

•

The cells around seed point are evaluated. The program searches for a combination of neighboring cells which can be merged to fulfil the area requirements of the event. Only the cells which have a common edge with the cell with seed point are considered. The adjacency matrix prepared in the data collection stage proved to be useful in this stage.

“The dual steps of subdivision and merging create a complex design space capable of creating a very large variety of design options that are guaranteed to accommodate the 40

programmatic needs of the event” (Nagy, Villaggi, Zhao, & Benjamin, 2017)

Fig 10: Primary operations carried out by the program for merging cells to fulfill the area requirements.

g. After allocation of events, shortest path was calculated to the major events and Food and Beverage zones. (F&B: red; Events: blue)

Figure 24: (a-f): Parametrization representation of actual project (Nagy, Villaggi, Zhao, & Benjamin, 2017)

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GD - OPERATIONS 6.1.5 Generating options based on primary goals- Buzz and Exposure (input parameters) The main objective of the design was to evenly distribute crowd. While high activity can be activating to attract visitors, from experience, it was agreed upon by the stakeholders that often congestion around F&B zones and amenities proves to be an inconvenience and causes disruption in the event.

Measures were developed to efficiently map Buzz and exposure on the basis of past events.” Both metrics were calculated using a novel static graph-based simulation method.” (Nagy, Villaggi, Zhao, & Benjamin, 2017) The following images highlight how foot traffic was evenly distributed by buzz and exposure mapping. (Nagy, Villaggi, Zhao, & Benjamin, 2017)

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Figure 25:Buzz parameter considers the spatial distribution of high traffic areas in the plan (Nagy, Villaggi, Zhao, & Benjamin, 2017)

43

Figure 26:exposure parameter accounts for average foot traffic around each booth (Nagy, Villaggi, Zhao, & Benjamin, 2017)

After defining input parameters, guidelines need to be established to ensure that the algorithm yields good results. Without setting guidelines, there is no way to know that optimal designs are being generated by generative algorithm.

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6.1.6 Evaluation of options-ensuring quality in quantity The designers set two guidelines to ensure that algorithm produced desired results. These two guidelines were more like trade-offs between two extreme conditions. They believed that a good design lies somewhere between the extremes. (Nagy, Villaggi, Zhao, & Benjamin, 2017) 1. Bias versus variance

This guideline accounts for the scope of design. Any model which is partial towards a particular solution defeats the purpose of iterative design approach and will end up giving too similar alternatives.

Similarly, any model with a high variance produces meaningless designs which differ to the extent that same parameters cannot be used to judge the optimization in iterations. Thus, randomness in a model needs to be controlled as a good design will have characteristics of both- it will generate different options but the scope would be limited. Design options generated therefore have to different but should seem belonging to the same species. (Nagy, Villaggi, Zhao, & Benjamin, 2017) 2. Complexity vs. Continuity This deals with the interaction of internal spaces and also compares input paramters to output design space metrics. Any generative algorithm should produce designs which are unpredictable, innovative and unconventional. But on the other hand, too much complexity can lead to inconsistency. Hence, design needs to accommodate unpredictability but maintain consistency in internal spaces.

To achieve these, the designers used a low-resolution sampling model to run simulations for the placement of spaces with respect to each other. This was first done for the initial set of 3 parameters and

then carried on with the second set for 22 parameters. The design spaces were ranked in terms of buzz. The simulation and plotting of spaces revealed that high and low scores were gradual in some zones 45

and abrupt in others. Thus, proved that the outcomes were produced complexities locally but maintained continuity as a whole.

Figure 27:x and y axis represent buzz values; z axis shows their corresponding exposure models (Nagy, Villaggi, Zhao, & Benjamin, 2017)

46

POST GD OPERTIONS 6.1.7 Design Optimization- Ranking outcomes to understand the choice This step involves using Generative Algorithm (GA) for finding optimal solutions. The computer generates random simulations initially and asks the user to rank them in order to understand the user’s preferences. On the basis of these preferences, the algorithm is run to discover suitable mutations.

Genetic algorithm, NSGA-II, was used with, second generation, 320 highest performing designs from the earlier stage. This gave an edge to the program and prevents it from wasting power on non-viable options.

(Nagy, Villaggi, Zhao, & Benjamin, 2017) 20,480 designs were produced during analysis stage (represented in gray). It was narrowed down to 32,000 in the optimization stage (represented in cyan). During optimization, the algorithm improved on its results by crossing over different combinations of high functioning

design options (represented in magenta). (Nagy, Villaggi, Zhao, & Benjamin, 2017) Out of these 38 options were presented to stakeholders and clients.

These were all high performing designs and fulfilled the objectives and operated within the tradeoff guidelines.

Fig 13: Design optimization and selection.

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Figure 28:Three selected designs, refined manually to accommodate suggestions from stakeholders

6.1.8 Conclusion Generative design served as a virtual assistant and helped complete months’ worth of work in a couple of weeks.

Figure 29:The finalized plan of the AU Exhibit Hall 2017

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6.2 Autodesk MaRS Office, Toronto Designed by: The Living Autodesk used Project Discovery, a generative design tool, to design MaRS Office in Toronto. The project is a conglomeration of human and machine learning. The project is 60,000 sq feet in area which is distributed over three floors. (Archistar, n.d.)

A similar process of Generate, Evaluate, Evolve and Refine has been used in

this project as well.

The main intention of citing this case study is to provide self-explanatory visuals which outline the generative design process over the course of the project.

Figure 30:Defining the boundaries of design space (The Living, 2018)

The above picture highlights the first step in any GD process, i.e., defining the extents of a project or highlighting its boundary.

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6.2.1 Data Collection and Analysis Stage

Figure 31:Identification and classification of design space requirements (The Living, 2018)

Data Collection was done to identify the type of spaces and compile an area analysis for the same.

Figure 32:Goal formulation on the basis of parameters (The Living, 2018)

The designers took a survey of about 250 employees to derive the input paramters and objectives of the design problem.

50

Figure 33:Prioritization of objectives. Setting up base objectives for running simulations. (The Living, 2018)

Among the six goals, two were prioritized over the rest. These two will be the part of the first set of algorithms used to run the simulation. The other set of algorithms will be used for design optimization and refining. The first set of algorithms were Adjacency preference and work style preference.

Figure 34:Flowchart of Generative Design Stages (The Living, 2018)

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6.2.2 Design Space Parametrization- Setting up constraints in design space

Figure 35:Defining design space boundary and existing columns (The Living, 2018)

Position of columns and boundary denied as pre-existing constraints.

Figure 36:Fixed Zones defined in design space (The Living, 2018)

Fixed zones defined in design space. All areas will be defined with respect of

fixed zones. Central spine also defined, along which different teams will be organized in the design space.

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Figure 37:Division of areas into sections (The Living, 2018)

Design space is divided into sections to accommodate the teams. Flexible paramters, called neighborhood seeds, introduced. Seed points are tentative

placement of regions occupied by different teams. Division of spaces, position of seed points is subjected to manipulation by generative algorithm, on the basis of areas required.

Figure 38:Manipulation in seed points and neighborhood boundaries (The Living, 2018)

Area of neighborhoods adjusted according to the area analysis done in data collection stage.

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Figure 39:Placement of components (The Living, 2018)

Components placed randomly according to the requirements of different teams.

Figure 40:Placement of components (The Living, 2018)

Most efficient arrangement of components selected by the algorithm to cater to the needs of each team’s region, without causing hinderance to the entire design space.

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6.2.3 Design evaluation by overlay of input paramters

Figure 41:Adjacency preference and circulation mapping in design space (The Living, 2018)

Figure 42:Workstyle preference (The Living, 2018)

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Figure 43:Buzz mapping for activation of shared spaces through individual and team movement (The Living, 2018)

Figure 44:maximizing productivity by reducing audio and visual distractions (The Living, 2018)

56

Figure 45:Daylight mapping to provide natural light in the working space (The Living, 2018)

Figure 46:Daylight mapping to assess heat gains (The Living, 2018)

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Figure 47:Exterior opening and context studied for providing positive outside views (The Living, 2018)

6.2.4 Design Optimization and exploration

The generative algorithm evolved 30,000 options on the basis of the input paramters. These were refined by adding complexity by incorporating all six input paramters. Further, they were refined by combining and assessing outcomes.

Figure 48:List of options presented to the stakeholders (The Living, 2018)

After careful selection, ten options were presented to the stakeholders and final plan was formulate 58

6.2.5 Final floor plan of MaRS Office

Figure 49:Placement of Specialty spaces (The Living, 2018)

Figure 50:Placement of meeting spaces (green) and specialty spaces (white) (The Living, 2018)

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Figure 51:Administrative fixed zones (orange) (The Living, 2018)

Figure 52:Location of facilities(red) (The Living, 2018)

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Figure 53:Final Floor plan of MaRS Office (The Living, 2018)

The project was able to take end users’ preferences into account and produced faster results with efficient utilization of space. It increased the sustainability and habitability of the space. Indirectly it increased the productivity of the employees as it provided them with an environment which stimulated discussions but didn’t create unnecessary distractions.

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6.3 Airbus Partition Designed by: Bastian Schaefer (Airbus Emerging Technologies) A partition was required to separate passenger compartment in Airbus A230 from the galley. This partition was required to be light in weight and have structural strength which could support two jump seats. Also, the partition was required to have interstices which could be useful to pass wide items through.

(Autodesk , n.d.)

Figure 54:Initial Sketches by Bastian Schaefer for the partition (Autodesk , n.d.)

On the basis of the requirements, initial sketches were put forward by the designer.

“Airbus’s bionic partition needed to meet strict parameters for weight,

stress, and displacement in the event of a crash with the force of 16g.

To find the best way to meet these design requirements and optimize the structural skeleton, the team programmed the generative design software with algorithms based on two growth patterns found in nature: slime mold and mammal bones.” (Autodesk , n.d.)

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Figure 55:Iterations of growth patterns generated by the software (Autodesk , n.d.)

Figure 56: Testing structural integrity of various options generated. (Autodesk , n.d.)

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Figure 57:Bionic partition finalized design (Autodesk , n.d.)

3D prototyping models were used to test the shortlisted design options for the bionic partition. The partition looks like a random lattice structure, but has maximized structural integrity while being extremely light. (Autodesk , n.d.)

Softwares used for execution

Figure 58:Softwares used to create the bionic partition (Autodesk , n.d.)

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6.3.1. Detail of Lattice structure Slime mold: lattice components connecting opposite ends to achieve structural integrity.

Mammal Bones: Secondary vertices strengthening slime mold components.

Mammal bones have varying density and thickness to withstand points of extreme stress and strain in the lattice.

Figure 59:Network 1 for slime mold segments (Autodesk , n.d.)

Figure 60:Network 2 for slime mold segments (Autodesk , n.d.)

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Figure 61:Network 3 for slime mold segments (Autodesk , n.d.)

Figure 62:Network 4 for slime mold segments (Autodesk , n.d.)

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Figure 63:Network 5 for slime mold segments (Autodesk , n.d.)

Figure 64:Mammal bones representation in the lattice (Autodesk , n.d.)

6.3.2. Execution and conclusion After finding the most efficient design, the design of bionic partition was 3d printed in 100 separate segments and assembled. After stress tests, the largest 3d printed aircraft component has been commissioned in A320 series aircrafts.

Generative design was here was used to discover an innovative, high

performance design alternative. The utilization in aircraft engineering proves that GD applications are interdisciplinary and versatile.

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6.4. NASA: Satellite Antenna Design NASA used generative design for optimizing the performance of their satellite antenna. The software was informed about the application of antenna and safety protocols it had to incorporate. GD produced an antenna which works twice as better as the original antenna. (Knemeyer & Follett, 2020) (Kowalski , 2016)

Figure 65:Antenna designed in 1960s (L); Antenna design by GD (R) (Kowalski , 2016)

Apart from AEC industry, GD has played a crucial role in product design and industrial design as highlighted by the examples cited above. With careful use, this can prove to be extremely useful in creating and executing sustainable designs.

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CHAPTER 7: Impact of Generative Design in Architecture and related fields

69

CHAPTER 7: IMPACT OF GENERATIVE DESIGN IN ARCHITECTURE AND RELATED FIELDS

7.1. Benefits of Generative Design From the earlier mentioned projects, it can be said that generative design is definitely an optimization tool which would change the face of designing altogether. Some of the merits of the GD approach are listed below: 1. Virtual assistant Generative algorithms act as team of virtual assistants to the designer.

These

algorithms

process

paramters,

rationalizes

data

from

surroundings, creates iterations, does the grunt work in one-tenth time required by any person. It enables the designer to focus valuable time and energy on the creative aspect of designing.

2. Design optimization Exhaustive iterations of qualitative options ensure design optimization.

It takes into account the end user preferences and can tabulate large number of input parameters and preferences simultaneously. GD produces

designs

with

maximum

efficiency

and

minimum

compromises.

Apart from optimizing design space, GD can also reduce/ optimize material quantities by redefining and reinforcing the structural integrity of structures as it did in the Airbus bionic partition.

New material technologies and environment friendly alternatives of building materials can be utilized to produce safe and sound structures.

Energy optimization can also be achieved by adaptable parametric façades in buildings.

3. Advancement in sustainable technologies and designs Sustainability in designs can be achieved as innovative solutions can be explored using generative design algorithms as NASA did for the satellite antenna design.

4. Improved workflow collaboration Interoperability has increased and conveying design information across softwares is easier. Generative algorithms can be processed in 70

Grasshopper or Dreamcatcher, can be refined in Dynamo and constructed in Revit for producing accurate material quantities and other building information.

5. Reduced chances of error Generative design softwares can be paired with CNC or 3D printing machines to generate prototypes which can be tested under real conditions for failure.

Not only prototyping, but precise execution can be carried out by using 3D models to generate actual size components, prepared with the precision of a computer’s and assembled on site using simple fastening techniques and lifting equipment.

6. These algorithms can also regulate Intelligent Building Systems and optimize the management of resources in a building.

Thus, Generative Design in architecture enables faster development of optimized designs, constructs them with precise execution and manages

them efficiently after their construction.

7.2. Possible concerns •

Substantial time is required for the skill development in generative design. Learning softwares requires patience and practice. However, it has been pointed out that according to the accelerated growth chart, the industry will soon embrace generative design. (Allen , 2016)

Figure 66:Accelerating growth graph (Allen , 2016)

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•

For the development of algorithms, a vast knowledge base of architecture and the co-related disciplines is required. Moreover, this also requires a preliminary knowledge of the working of algorithms.

•

A careful process is required to set guidelines for computation or the algorithm won’t yield viable options after algorithm simulation. This is a complicated task and only improves with ample experience in the field.

7.2.1. Monopoly of AI over designing

•

Complete reliability on softwares for designing might pose a monotonous dystopia, entirely ruling out the creativity, the intuitive nature of a designer’s mind.

7.2.2. Workforce replacement or changing role of architects?

•

Misconceptions exist in the field about generative design replacing architects. However, the case is just opposite. Instead of replacing architects, GD is providing exposure to them.

•

It is enabling architects to do more work in less time. GD has the potential to enable architects to take up jobs in different countries without passing the licensing tests as any design produced could be evaluated and optimized according to the building regulations of the country.

•

It is empowering them to take jobs in various other fields- coding, algorithm generation, design evaluation and most importantly research.

Research is the key part for GD to flourish. If stopped the algorithms

will become obsolete in time and would be unable to accommodate emerging challenges.

Architecture was never related to the study and construction of buildings. It is rather a portal which expands our mindsets to look beyond the curtain of oblivion and habituation. Architects are valued for their uniqueness of vision; any skill can be developed over time. The field has been demanding an interdisciplinary approach from its designers, generative design is simply giving us an opportunity to try.

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CHAPTER 8: Summary and References

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CHAPTER 8: SUMMARY

Chapter 1 Introduction to Generative Design This chapter introduces the concept of generative design. It also outlines some reasons why the implementation of this approach will be beneficial to the architectural practice. It outlines the aim, objectives, scope of work and methodology used in the study.

Chapter 2 Developments in Generative Design in 1960s This chapter discusses the milestones in the development of generative design approach- the emergence of generative art, development of computational architecture (use of CATIA and B-splines modeling development) and the early works of Celestino Soddu- the first person to coin the term generative design.

Celestino Soddu’s initial hypothesis and its basis is discussed in detail. The hypothesis focusses on the mathematical explanations for designing with the help of softwares. It deals with defining the position of a point in the 3D space of software and controlling its movement and its transformations. Soddu postulated the minimum requirements for generative design approach•

Starting Point,

•

Logical Structure (set of algorithms which define the movement of starting

point-

mathematical

expressions

influencing

spatial

arrangement), •

Generative engine (evaluates and evolves algorithms and henceforth creates modifications in design forms)

•

an understanding of Context and project (introducing subjectivity in programming to make every project have a unique identity)

Soddu’s initial hypothesis is an attempt to formulate basis for what we understand today as ‘parametricism’.

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Chapter 3 Early Software Developments And their Principles This chapter contains three sections. The first, outlines the learning derived from past developments and addresses the challenges and the requirements for creating a generative algorithm. The second, defines the development of Basilica- a software used to generate iterations of medieval towns. The third section discusses the underlying principles for the development of artificial

DNA, incorporated in a software called Argenia which is capable of making subjective decisions.

The first section expresses the need for a database which include studies of architectural precedents. This database would be required for giving the computer a ‘consciousness’. In other words, teaching various possibilities of form manipulation to the machine, allowing it to observe and derive patterns from existing knowledge. The second requirement would be to learn and evolve its algorithms, thus, producing progressive logics. This would help the initial idea grow and introduce complexity. Complexity or overlap in algorithms would generate more than one possibility. This would induce a sense of subjectivity in the design forms produced. Complexity and subjectivity both would contribute to unconventional, unpredictable outcomes.

The second section discusses development of Basilica which was used to run simulations to represent Giotto’s ideas of medieval towns. However, the base algorithm was required to be rewritten and adapted to every design problem

specifically.

So, the third section talks about development of an artificial intelligence which would run on a base algorithm capable of adapting itself to the design problem and producing iterations as preferred by the user. The software fulfilled what it was meant to be but it was limited by the resources of its time. The generative design approach didn’t gain momentum but the idea did. The generative design idea has been worked upon for decades since then and the foundation principles were perfected to introduce it again into architectural practice.

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Chapter 4 Projects by Celestino Soddu on Basilica and Argenia using Generative Algorithms Even though generative design approach seemed far off then, Celestino Soddu in collaboration with Enrica Colabella kept developing their idea and presented several designs in conference proceedings and exhibitions. This chapter discusses the projects designed on Argenia. In all these projects, a precedent of existing city character was input to the machine and options were generated on its basis.

Chapter 5 Evolution of Generative Design Understanding The development of Parametricism and BIM softwares have aided the smooth transition to generative design. These strengthened the backbone of generative design approach. This chapter discusses the contribution and role of parametric and BIM softwares in achieving algorithmic/generative modeling.

This also highlights research done by Autodesk in this field and explains their years of work and the steps they took to achieve successful application of generative design approach in architecture.

Chapter 6 Application of Generative Design This chapter uses examples to enlist the steps that have been used in designing

projects through generative design.

The example of Autodesk University Exhibit Hall (2017) has been discussed in detail. This presents a step-by-step approach to generative design, highlights the set of algorithms used and their stratification and also addresses the challenges faced by them during the process.

The Autodesk MaRS Office design is explained to show the importance of end user experience in any design problem. Generative Design approach incorporates Meta-Heuristics to optimize user’s design experience. Designs produced might not be symmetric or follow thumb rules in plan but the program ensures that they are comfortable to the user.

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The example of Airbus partition is discussed to prove that unconventional designs produced by GD can be extremely efficient. Also, this example shows that there is no unified approach present in applying generative design. It can use a combination of softwares, each used for a specific function. The choice of softwares used depend on the kind of design problem and its challenges.

For running iterations however, separate evolutionary algorithms are used, one of these mentioned is the NSGA II.

On the basis of examples, a tentative workflow flowchart has been stated at the end of this chapter. This however might or might not be applicable to all design problems as every problem is unique due to its challenges.

Chapter 7 Impact of Generative Design in Architecture and Related Fields This chapter highlights the benefits of implementation of generative design in architectural practice. This also includes some of the concerns about GD, that I came across while reading blogs by individuals. These concerns are expressed along with my opinions on the topic and the knowledge I’ve collected from research and lectures conducted by Autodesk forums and University. Change in the role of architects and designers is inevitable. Generative Design however, is giving designers an opportunity to expand their horizons and their prospects.

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Figure 67: Tentative Workflow of Generative design approach derived on the basis of examples.

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