Generative Design: The Creation of a New Architect by Paul Burghelea

Generative Design: The creation of a new architect How will generative design influence architecture?

Generative Design: The Creation of a New Architect

Paul-Andrei Burghelea Signature of Author: ____________________________________

Certified by:

BA in Architecture at University of Greenwich (2017-2018) ‘to be presented to the Department of Architecture and Landscape at the University of Greenwich as part of the BA (Hons) Architecture course’

____________________________________

‘Except where stated otherwise, this dissertation is based entirely on the author’s own work’.

Accepted by:

October 2017

____________________________________

Word Count: 9343

Dissertation Advisor: Dr. Shaun Patrick Murray Phd M.Arch BA ARB Architect

Acknowledgements I would like to thank to my dissertation tutor, Shaun Murray, for his guidance which helped me to develop my ideas. I want to express my gratitude to Andreea Luca (Technical Engineer) and Dan Matei (General Director) - Ecotech Romania, for the access offered to their production workshop that helped me to create and develop the experimental device. And I also would want to thank Francesca C. Immirzi for her help at proofreading.

Generative Design: The creation of a new architect

I.1 Contents I./// Introduction → p. 0.0

C.2.1

Accelerated Growth→ p. 37

I.1 Aknowledgmenets → p. 7

C.2.2

Autodesk Generative Design → p. 39

I.2 Contents → p. 9

C.1.2

“The Living”→ p. 41

I.3

Reserch Methods Statement → p. 11

I.4 Abstract → p. 13 I.5

Tipping Point → p. 15

C.1./// Chapter 1 → p. 17 This chapter answers to the questions as: “When it appeared?”, “What is it?” and, “Why generative design be successful?”

C.1.1

What is Generative Design? → p. 19

C.1.2

Novellty or Commodity? → p. 23

C.1.3

Why should it not fail again? → p. 23

C.1.4

Would Generative Design be accepted by architects? → p. 25

C.3./// Chapter 3 → p. 45 This chapter answeres to questions as: Is softare capable of simulating human traits such as creativity? Is artificial intelligence able to replace architects? C.3.1

Dystopia or Utopia → p. 47

C.3.2

Workforce Replacement → p. 49

E./// Experiments → p. 51 This part contains two separate self developed experiments that allowed me to accelerate my design development and representational skills using generative design and robotics as a main driver. E.1 Voronoid Structure Flat Arrangements→ p. 53

C.1.5 Can Technology Change an Industry? → p. 27

E.2

C.1.6

C./// Conclusion→ p. 61

Does Generative Design Hold Real World Value? → p. 31

C.2./// Chapter 2 → p. 35 This chapter explains the workings of the Autodesk Generative Design as presented in the Amazon and Autodesk University Lectures.

Generative Design: The creation of a new architect

The Architectural Turring Test→ p. 57

A./// Appendix → p. 65 A.1 References → p. 67 A.2 Image Sources → p. 71

Research Methods Statement The dissertation aims to analyze the acceptance, usability, and effects of generative design in architectural practices. The Bifurcation Theory is used to present a clear plan of the elements that have influenced the development of generative design. The dissertation considers generative design as a tipping point in the accelerated growth of technology in the Architecture Engineering and Construction Industry. The beginning of generative design in architecture will be defined using books, such as ” Fenland Tech: Architectural Science in Post-war Cambridge” by Sean Keller and “The Automated Architect“ by Nigel Cross. The technical difficulties of “computational architecture“ (as it was called in the 1960’s) will be pointed out to conclude if they still can be considered drawbacks today. Moreover, social analysis from the Rensselaer Polytechnic Institute will be used to predict the probability of generative design to go mainstream. The Industry requirements will be formed by researching the methodology that Autodesk uses to generate architectural design. This information will be extracted from the lectures offered by the Autodesk University 2016, Autodesk University 2017. Furthermore, the formed concept of the industry mindset will be polished using cuff’s “Architecture: The Story of Practice“ book. (Cuff, 1992) Data from The National Endowment for Science, Technology and the Arts will be used to predict the possibility of the architecture industry to become automated either by the use of Generative Design or Artificial Intelligence. (Hasan, B., Carl, F. and Michael, O., 2015) The automation will be further discussed over concepts, such as Fully Automated Luxury Communism, Accelerationist politics and the lecture of Sebastian Thru (artificial intelligence researcher) from The Technology, Entertainment and Design conference 2017. (TED, 2018) Finally, two experiments will be developed to prove the advantages of generative design and automation. The first one can be described as a generative algorithm that creates space divisions in any given space. It uses space syntax theory to make rooms and door openings that This algorithm will be programmed in Grasshopper and rendered using Rhino 5. The base logic of the algorithm in Space Syntax Theory. (Nourian, P., Rezvani, S. and Sariyildiz, S., 2013) For the second experiment research into the Engineering and Manufacturing Industry was made using as guidance the staff from EcoTech Romania. They offered training in software use, manufacturing requirements and device development. The second experiment is the automation of the hand drawing process by using a self-developed pen plotter. The purpose is to replicate the Turring Experiment with an architectural application. This will propose a method to test if a given generative design can simulate/ imitate human-like design qualities.

Generative Design: The creation of a new architect

Abstract In the 1960’s, a new method of design called “computational architecture“ emerged in the AEC Industry. The design approach faded from use until recently, when it was renamed as “generative design“. Moreover, substantial advancements have been madee in simulation technology and artificial inteligence. This increasded the cyhance for generative design to become a usefull tool than in the past. At the 2017 Autodesk University lectures the research group “The Living” has presented the concept of generative design in that was used to create the Autodesk offices from Toronto. Generative design is presented as a novel and objective approach that can eliminate subjectivity from the design process and reduce building costs and design time. In this context, I am interested in how generative design could influence an architect in a practice. My dissertation explains the complexity of this new technology and its importance in the architecture design devepeloment and examines the chance that it has to become mainstream. The bifurcation theory will be applied over the accelerated growth from architecture. The scheme will allow the creation in order to offer a projection for the future of generative design. Two experiments will be carried out to inform my opinion and allow me to explain the possible applications of generative design. The first experiment consists in a mass design algorithm that is capable of generating a large number of flat arrangements based on a voronoid structure. The second experiment is an adaptation of the Turring Test, created by Alan Turring, with the purpose of physically drawing and comparing the results with other architecture students.

Key Words Generative Design, Automation, Architecture, Artificial Intelligence, Tipping Point

Generative Design: The creation of a new architect

The Bifurcation Theory 1945

1950

1960

1970

1980

1990

2000

2010

2015

2020

2025

Data O Sebastian T

Autodesk Gene The Living: Autodesk MaRs Offices

Autodesk University 2017 Lectures Amazon Re: Invent 2017

Autodesk University 2016 Lec

Accelerate Growth in AEC

Fully Automated Luxury Communism NESTA Automation Study Accelerationist Politics Data Manipulation

Nate Hollandâ&#x20AC;&#x2122;s Thesis The Bifurcation Effect

Arab Spring

David Copeâ&#x20AC;&#x2122;s Music Algorithm Kind Code Patent University of Toronto Creativity Study Celestino Soddu Seagram Building Boolean Formulation Second World War

Data Gathering Frank Gehry CATIA

Apple II Launch Sketchpad

Computing, Machinery and Intelligence

Optimization Thrun

erative Design

ctures

Figure 1 Bifurcation Theory Applied to The Accelerated Growth from The Architecture Industry

Tipping Point

In the Bifurcation Theory, a moment in time is characterized by the sudden shift in behaviour, arising from small changes, is defined as a tipping point. (Poincaré, 1885) Recently, the term has gathered social aspects. The tipping point is also when a large group of people change their behaviour by adopting a previously rare practice, as seen in Granovetter’s “Threshold Models of collective behaviour”. (Granovetter, 1978) In architecture, a tipping point is represented by an idea, technology, discovery that started an accelerated growth or decrease of the domain. It still follows the mathematical rule of increase/decrease but it also follows social aspects. The concept is applied to the accelerated growth of technology in architecture with a focus on generative design. Each chapter will begin with a description and the time-slot of the graph that will refer to the past, present or future of the bifurcation theory. The full graph is represented above.

Generative Design: The creation of a new architect

2010

The Bifurcation Effect

Arab Spring

1945

Kind Code Patent

2005

1945

2000

1945

1995

Celestino Soddu

1945

1990

1945

1985

1945

1980

Frank Gehry

1945

Apple II Launch

1975

1945 Seagram Building Boolean Formulation

1965

1960

1955

Computational Architecture

1970

1945 Sketchpad

1945

1950

1945

Second World War

1945

C.1./// Chapter 1

Different changes in the Architecture, Engineering and Construction Industry such as technological advancements that have allowed for concepts as automation, parametricism and proceduralism have contributed to the formation of generative design in architecture. This chapter answers to the questions as: “When it appeared?”, “What is it?” and, “Why generative design be successful?”

“The interests of humanity may change, the present curiosities in science may cease, and entirely different things may occupy the human mind in the future.” John von Neumann (Stanislaw, 1958)

Generative Design: The creation of a new architect

What is Generative Design? Generative Design is currently an evolving concept that has not reached maturity. Even if it has not been defined at the moment, it is a recognized idea by architectural and design researchers. (Shea, Aish and Gourtovaia, 2005) Celestino Soddu, one of the parents of generative design in architecture, defined it as: “… a concept-software that works producing three-dimensional unique and non-repeatable events as possible and manifold expressions of the generating idea identified by the designer as a subjective proposal of a possible world. This Idea / human creative act renders explicit and realizes an unpredictable, amazing and endless expansion of human creativity. Computers are simply the tools for its storage in memory and execution.” (Soddu, 1994) Fig. 2 Celestino Soddu: New York City Imagination

Later, he added: “Generative Design is a project approach that can enhance own creativity, own designer identity, the character of own being an architect. It’s like having a large team of architects working for us, each of which develops possible variations of every detail and every overall layout.” (Soddu, 2017) Celestino percives generative design as a tool that expands the architect’s capacity of designing. He considers generative design for its aesthetic qualities. This technology was developed for its “practical advantages” in design. In 2007, a patent for “Method and system for automated design” was submitted by “Kind Code”. They proposed a new generative system developed on the concepts from “Massachusetts Institute of Technology (MIT) Conference Design, Computing and Cognition ‘04”. The proposed system would generate many iterations of a given object, thus automating the process. (Kind Code, 2007) (The concept of automation will be analized in depth in the second chapter at page 31) The automation of the process is non repetitive, offering a large vartiation of iterations. In “Creative evolutionary systems”, generative design was defined as:

“… 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 and Corne, 2002)

Generative Design: The creation of a new architect

Idea

Abstraction

Rule Algorithm

Formalization and (starting) parameters

Source Code

Interoperation by the computer

Output

Modifies rules

Designer

Modifies source code or parameters

Designer judges the output

Furthermore, in the development of the concept Shea defined the objective for the generative systems: “generative design systems are aimed at creating new design processes that produce spatially novel yet efficient and buildable designs through exploitation of current computing and manufacturing capabilities” (Shea, Aish and Gourtovaia, 2005) Most recently, Autodesk considered generative design applied to architecture, engineering and construction industry to be: Figure 3 The Generative Design Process Diagram

“… a technology that mimics nature’s evolutionary approach to design” that” … starts with your design goals and then explores all of the possible permutations of a solution to find the best option. The process lets designers generate brand new options, beyond what a human alone could create, to arrive at the most effective design.” (Autodesk, 2017)

In all definitions there was a consistent use of the next defining characteristics: 1. empowerment/ enhancement of the architect’s ability of design 2. automation of the design process for enchanced productivity 3. flexibility of the design methods and ability to base the design choice on objectivity rather than subjectivity In this dissertation, architectural generative design is considered a generative process (Figure 3) defined by an architect using constraints and goals. The results are unknown and are defined from a multitude of iterations influenced by the goal-constrained pre-defined system.

Generative Design: The creation of a new architect

Novellty or commodity?

Generative design is not a new idea. It started in the 1960 as “automatic architecture”. (Daniel Davis, 2015) In his article:” Fenland Tech: Architectural Science in Post-war Cambridge”, Sean Keller explains the context where the concept was developed. He traced “computational architecture”, to the beginning of the Second World War. The next problems where raised:

“The extremities of war had forced to the surface many doubts about architecture as a significant modern profession: Did architects possess special expertise? Was their expertise objective or merely based on taste? In times of real need, were architects necessary? In short, was architecture serious business?” (Keller, 2006) Figure 4 “Mies van der Rohe: Seagram Building was the subject of a Boolean formulation in a 1972 paper from the University of Cambridge.” (Daniel Davis, 2015)

In the post-war time, efforts were made to legitimize architecture by adding mathematical principles into the design methods. (Keller, 2006) This approach rejected the “intuitive skill” in design and brought “confusion”, “hallucinations,” “extravagant and empty images,” that “threaten architecture and planning.” (Keller, 2006) Nigel Cross described the outcome of this situation in his 1977 book, “The Automated Architect”. He focused on the description of a software that was capable of optimizing the walking distances between rooms, in the time when communication was not that advanced. (Cross, 1977) The main motivation of the endeavour was that architects wanted to base their designs on statistics and observed behaviour. This allowed them to legitimize their decisions through mathematics of topology and graph theory. (Keller, 2006) As expected, the approach was a failure, since the optimal walking distance does not guarantee the success of a building. The idea was recently resurected, as many global architecture firms have embraced it. (Stocking, 2011)

Why shoud it not fail again?

The approach from 1960’s was deemed to a failure. A large range of factors must be considered in architecture. In 1977, the Apple 2 computer, with only 128 kb was launched. Computer power has increased exponentially since. Also, breakthroughs in simulation technology and algorithmic advancements have increased the factors that can be processed by computers. (Autodesk, 2014) Now we can simulate factors starting from sunlight, humidity and scaling down to room arrangements as integration coefficients. (see the “Voronoid structure flat arrangements” from page 52)

Generative Design: The creation of a new architect

Would generative design be accepted by architects?

The concept of “computational architecture” as described by Sean Keller, has faded from our architectural knowledge. Nowadays, generative design has a considerable chance of becoming maistream.

Figure 5 Arab Spings Image from Egypt

An important aspect of architecture is the social spread of ideas. New concepts at their time, known now as modernism, constructivism, neoplasticism or expressionism, have gained fast recognition in the 1900’s. Heynen Hilde classifies them as innovative, radical (avant-garde) and generally accepted (mainstream) architecture, but the structure behind their “growth in popularity” was never clearly defined. (Sennott, 2004) In a 2011 study, scientists at Rensselaer Polytechnic Institute have discovered the integration of the bifurcation effect in the accelerated spread of ideas. The scientists, that were also members of the Social Cognitive Networks Academic Research Centre (SCNARC) at Rensselaer, discovered that the popularity of an idea grows exponentially when a certain percentage of the population strongly believes it. (Xie et al., 2011) As said by SCNARC Director Boleslaw Szymanski, and Claire and Roland Schmitt: “When the number of committed opinion holders is below 10 percent, there is no visible progress in the spread of ideas. It would literally take the amount of time comparable to the age of the universe for this size group to reach the majority…”, but, “Once that number grows above 10 percent, the idea spreads like flame.” (Xie et al., 2011) An important aspect of the discovery by DeMaro on the Rensselaer Polytechnic Institute website is: “…the percent of committed opinion holders required to shift majority opinion does not change significantly regardless of the type of network in which the opinion holders are working.”, moreover ”…the percentage of committed opinion holders required to influence a society remains at approximately 10 percent, regardless of how or where that opinion starts and spreads in the society.” (DeMarco, 2011) The understanding of this concept is exemplified by Szymanski in the case of the political events from Tunisia and Egypt (2011, Arab Spring): “In those countries, dictators who were in power for decades were suddenly overthrown in just a few weeks.” (Xie et al., 2011) Applied in architecture, the research explains the fast integration of certain tehnologies and ideas. As is the example of 3D printing and parametric design in teaching and practices. (Schumacher, 2009)

Generative Design: The creation of a new architect

Can technology change an industry? Tracked back to its infancy, architecture can be defined as a multi-layered discipline focused on creativity, collaboration and the pragmatic union of arts and technology. (Cuff, 1992) For an extended period, architecture was mostly recognized for its artistic qualities. During the last half of the 20th century, the AEC Industry introduced a series of managerial and technological improvements. (McPhee et al., 2013) (Yan and Damian, no date). The aim was to convert architecture into a business, to reduce the costs and increase the profits. (McPhee et al., 2013) (Yan and Damian, no date)

Sketchpad acronym that stands for “Computer Aided Three-dimensional Interactive Application”

CATIA acronym that stands for “Computer Aided Three-dimensional Interactive Application”

CAAD acronym that stands for “Computer Aided Architectural Design”

In 1963, Ivan Sutherland created Sketchpad. This software was designed as a way to change the profession of an architect into a software engineer. For most architects, this moment is considered the first interaction between an architect and a computer. Sketchpad was invented in the right moment. At that time, there was no efficient way to draw, share and manage project drawings. With this in mind, Autodesk converted Sutherland’s idea into a commercial product called AutoCAD R1. The software solved the previously mentioned issues by offering one of the first computer aided design solutions for architects. The product known as Autocad R1 quickly became the preferred choice for practices to increase productivity. To remain competitive in the industry, architects acknowledged the need for technology. A more important example is probably the use of CATIA for architectural products by the Gehry Partners, LLP. In 1980, the firm was contracted with the design of a fish-like construction in Barelona, Spain. The concept required to be constructed from an inner structure covered with sheet metal pannels. (Bruton and Radford, 2012) They faced problems due to the high costs and representation limitations. Even if 3D architectural software had been available at the time, it was programmed to fullfill the conventional needs of practices. They were limited to linear form creation. The curbed shaped volumes, characteristic in Gehry’s designs, could not be represented. Even if 3D CAAD is a competent design technology, it was limited to conventional architectural shapes. (Bruton and Radford, 2012) Moreover, the Guggenheim Museum from Bilbao, Spain raised the same problem. The search for a solution was extended to the aerospace and automotive industries. CATIA, a software developed by Dassault Systems was a capable software at the time, able to define complex 3D enviroments. The capacity of sending data directly to the producer and constructers speeded up the process of construction. (Bruton and Radford, 2012) James Glymph, the advisor that introduced CATIA to Gehry, said that:

Fig. 6 Ivan Sutherland using Sketchpad in 1962

“I had to come up with a method of cladding with one panel type that had to be able to change shape. That panel, shape like an accordion, could then be predetermined in the computer.” (Van Bruggen, 1997)

Generative Design: The creation of a new architect

Giving to the manufacturers additional information allowed them to work faster, cutting wasted time due to the reduced interoperability, creating a more accurate results. (Bruton and Radford, 2012) Gehry said that:

Fig. 7

“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 prevented inordinate premiums.” (Van Bruggen, 1997)

View of the complex faade from the Guggenheim Museum from Bilbao, Spain

He was impressed by the amount of information that an engineering software (CATIA) could hold, and satisfied that he could draw his vision into a standard industrial platform that made his design possible. Gehry said that “…we want to guarantee that we can build that vision and we want to guarantee that we can build it at a price that the owner can afford” (Dassault Systèmes, 2010) The adoption of previously foreign technology to architecture offered Ghery and advantage over competition making him to realize that: “… we got powers…”, “… that architects don’t usually have.” (Dassault Systèmes, 2010)

Z Z

1 1

Subdiviion

Polygon Mesh Non-uniform rational B-spline

Fig. 8 Diagram showing the advantages of CATIA in terms of geometry construction. It used Non-uniform Rational B-splines that allowed for more fluid forms to be designed

Generative Design: The creation of a new architect

Does Generative Design hold real-world value?

The following three concepts can be considered modern influences for the generative approach in architecture. From a practical point of view, it shares a technical foundation with parametric design, theoretical and aesthetic principles with the procedural approach and practical value with design automation. Parametricism shares the use of software and design strategies. Algorithmic software (i.e.. Grasshopper and Dynamo Studio) are the preferred choices by practices for the design of parametric models. The generative design adds the practicality of a generative solver, as Galapagos. Another difference in generative design is the definition of the end result by the designer. An architect will partially or completely know the end shape of the parametric design. In comparison, the generative method requires the designer to define the goals and constraints. The final or incipient design form is not defined.

Fig. 9

Michael Hansmeyer defined procedural design in the (Princeton School of Architecture, 2014) to be a “technology driven” design process that is “…more about changing the procedure”. He confidently states that computers can make architecture, and that this design procedure that has the capacity of creating a product that would otherwise be “un-drawable“ by a human due to the complexity of the forms. Hensmeyer’s approaches proceduralism through a subdivision algorithm. He uses platonic solids to create subdivisions that result in complex forms. (Princeton School of Architecture, 2014) He acknowledges that he does not have a criterion for choosing the “most successful specimens”. The algorithm “…produces a different form every time…” but he does not know if he is selecting forms for beauty or utility. In the procedural approach, the designer predefines/ defines the method of design in terms of rules and procedures and allows the algorithm to generate the form. (Shah and Mäntylä, 1995) The procedural generation of architecture is related to generative design. It shares all the afore mentioned characteristics, but where generative design tends to differ is the selection of algorithms and the use of environmental goals and constraints to influence the final product.

Mihael Hansmeyer: Subdivision of Platonic Solids

Generative Design: The creation of a new architect

Prof Mikell Grover defined automation as:

“The technology by which a process or procedure is performed without human assistance. Humans may be present as observers or even participants, but the process itself operates under its own self-direction. Automation is implemented by means of a control system that executes a program of instructions. To automate a process, power is required to operate the control system and to drive the process itself.” (Groover, 2010)

Fig. 10 M. Fuksas : Interior view of the Bao`An Airport’s facade from Shenzhen

///Design/

In architecture, the concept describes tasks that are performed by physical (robotics, manufacturing) or logistic (computer algorithms) means. Dr. -Ing Milos Dimicis is familiar with the automation process of architecture. He describes automation to be a key part of the future of architecture, which eliminates repetitive work, that otherwise would be overwhelming for architects. (Dimcic, 2011) An example of design automation process can be considered the facade from the Bao`An Airport in Shenzhen, designed by M. Fuksas. (Dimcic, 2011)

Computer Aided Design

3D Model

Finite Element Method

2D Drawings

C++

CNC Files

Fig. 11 Dr. -Ing Milos Dimici’s Diagram of his automation process and design production

Generative Design: The creation of a new architect

Data Optimization

2020

2019

2018

2018 Amazon Re: Invent 2017

2017

2016

The Living: Autodesk MaRs Offices

Autodesk University 2017 Lectures

Accelerate Growth in AEC

Autodesk University 2016 Lectures

2017

2016

2015

Data Manipulation

2015

2014

Project Dreamcatcher

2014

2013

2012

2011

Nate Hollandâ&#x20AC;&#x2122;s use of generative design

2010

2011

2010

Data Gathering

C.2 Chapter 2

The Extensive research in CAAD, done by Autodesk, leaded to the development of generative softwares. This chapter explains the workings of the Autodesk Generative Design as presented in the Amazon and Autodesk University Lectures.

“…everything important to humanity has been invented in the last 150 years… the peace of the inventions has gone up, not gone down in my opinion…” TED (2018)

Generative Design: The creation of a new architect

Internet of Things Robo�c Construc�on Genera�ve Design AI BIM Photogrammetry

Interoperability Algorithmic Modeling

CAD

Hand Drawings

<-1950

1960

1970

BIM Parametricsim

1980

2000

2010

2020

///Data/

gatherering

manipulation

optimization

Static modelling (3Ds Max, Maya, )

Algorithmic editors (Dynamo, Grasshopper)

generative editors (Galapagos, Dreamcatcher)

Human Guided Craftsmanship

Computer Assisted Craftsmanship

1-10 iterations

1-100 iterations

Data Interoperability

1-1000 iterations

Accelerated Growth In the 2016 Autodesk University lectures Bill Allan, co-founder and principal at EvolveLAB BIM Consulting, described the changes that architecture will go through in the next three to ten years. In his lecture, called” The Future of BIM Will Not Be BIM”, he represented the concept of accelerated growth in the A.E.C. industry. Futurology perceives accelerated growth as part of the accelerating change, which marks the increase in technological development through a period in history. Since architecture is a technological domain, the same logic can be applied. (Allan, 2016) Fig. 12 Accelerated Growth in Architeture, Engineering and Constrution Technology by Bill Allan

According to Allan, architecture was in a “data gathering time” until approximatively 2015. Building information was manually added into projects, and represented using static modelling. We recreated iterations manually by remodelling following new requirements produced by context, authorities or clients. Most elements that were sent to the manufacturers needed to be similar, for them to reduce manual labour and thus lowering building costs. It is arguable that Frank Gehry, LLp was one of the innovators of this period. “We are now in a data manipulation time”. Algorithmic editors such as Grasshopper have been used by architects to manipulate large data sets . In a step forward, architects integrated Building Information Modelling with algorithmic modelling, through tools such as Dynamo, to speed up the design process. Architects can, using this method, exponentially increase the ammount of work. Moreover, they have not been limited by manufacturing costs in the same manner since computer assisted craftsmanship became mainstream. (Allan, 2016)

Fig. 13 Bill Allan’s concept of data in architecture

We are moving into a “data optimization time”. Allan said: “In contrast, rather than manually drawing walls, doors, and columns for what we think is a good design, we will feed the computer “rules” instructing it to give us a building’s optimal footprint, structural load capacity, and thermal performance. Things that usually took months will be easily done in a day.” This is already possible today using extensions available for algorithmic editors, such as Galapagos for grasshopper. Also, the interoperability and workflow between programs has been extended. In most of the cases, work may be transfered from one program to another. Nate Holland produced in 2011 a generative sky-scraper in Seattle for his thesis. (Holland, 2011) His approach was presented in the Association for Computer Aided Design in Architecture lecture from 2011. (ACADIA, 2011) The resulting building can be described as commercial and pragmatic. The main design constraints were: lot size, solar data, views to outside and accessibility to transports. The algorithms used the data to generate the final results, and offered a large range of iterations. (ACADIA, 2011) Theoretically it is possible to generate a building. Is it possible to create a project that will be built using generative design?

Generative Design: The creation of a new architect

Autodesk Generative Design

Fig. 14 The presentation of Brian Mathews at Amazon Re:Invent 2017

It is fair to say a comparison of current widely used techniques and generative methods would point to an advantage of the latter in terms of material reduction and cost reduction. (Allan, 2016) Autodesk predicte, in 2014, the importance of generative design and invested in research with the “Project Dreamcatcher”.(Autodesk, 2014) The company marketed the project as the “…the next generation of CAD”, a software that “…could generate thousands of design options that all meet your specified goals…”, through an experimental platform focused on generative design. The Autodesk team sees their project as of way of advancing design adding different competencies to the architect’s toolbox. A series of publications as “BIM-based Parametric Building Energy Performance Multi Objective Optimization” (Das, Zolfagharian and Haymaker, 2016) were released by members of the project and offered promising insights into the future features that the software might offer. In 2016 Autodesk released a trailer showing the capacities of a generative design software that they are currently working on. (Autodesk, 2016) Arthur Harsuvanakit announced the commercial release of Dreamcatcher, labelled as Autodesk Generative design by the end of 2017 (Harsuvanakit, 2017) Snippets of the capacities of the software can be seen in Autodesk Netfabb 2018 Ultimate release, but the full generative package has yet to be published. At Amazon Re:Invent 2017, Brian Mathews presented Autodesk’s views and aims in generative design. Brian is the Vice President of Autodesk’s platform engineering. In his presentation, generative design is seen as a favourable consequence of the increased computing power of the last decade. As Brian said: “Every time when we increase the scale of computing a new phenomenon emerges…” (Mathews, 2017) Brian believes that “…with generative design the computer becomes our partner…” and that “…it is divergently thinking, it is exploring creatively the possible design…”, so design becomes more meaningful by using the qualities that the computer offers. The area that this partnership is needed is where “… we have to make judgements between potential, optimal solutions.”, where “… we want to make a trade-off between things like safety versus costs or weight, or weight versus environmental impact…” (Mathews, 2017) On the impact that generative design will have over the design industry, the Autodesk team considers that “… its’s still just a tool, but unlike the microscope and telescope that lets us see the world as it is, the cloud allows us to see the worlds that could be.” (Mathews, 2017) Autodesk has been clear in presenting the capacities of generative design in theory, but are these concepts also practical?

Generative Design: The creation of a new architect

Views to Outside

Daylight

Low Visual Distraction

Interconnectivity

Work Style Preference

Adjacency Preference

Definition Measurement of travel distance to preferred neighbours and amenities

Measurement of suitability of neighbourhood to team preferences. Determines how closely teamâ&#x20AC;&#x2122;s preference and weighting of ambient conditions (light and activity) are met by their selected neighbourhood

Measurement of congestion. Determined by cross-referencing simulated movement paths with computed traversability data for given space

Measurement of negative visual activity from individual workspaces. Tabulates the number of co-workers visible from a given workstation

Measurement of daylight levels in workspaces and amenity spaces. Daylight analysis uses industry-validated methods for calculating light levels and utilizes LEED v4 standards for evaluation and scoring

Measurement of exterior views from circulation and workspaces

Output

“The Living” At the 2017 Autodesk University Lectures “The Living” research group used the generative approach to design the Mars Autodesk offices. The design of the building, located in Toronto, was completely generated using environmental data (sunlight, view to outside) and workers preferences (adjacency, work-style and distractions) This allowed them to create a totally objective architecture. Their scheme can be summed up in the five following stages: Constrains : The team sets a series of constrains that the software is not allowed to break. In this case the constrains were represented by meeting rooms, social spaces and the number of people that would work in the building. The constraints change in function of the desired results.

Fig. 15 Table of the goals designed for the Evolutive stage

Data : After defining the constraints, the team chose the data. Data is the variation factor of the generative process. The constrains will be changed and adjusted according to the changes in data. An important part is to understand the different configurations required for a trade-off to achieve the best results in an area. So the best organization for offices might not also be the best configuration for daylight maximization for the whole construction. Generate :The first stage consists in the creation of a geometric system to contain the rooms. This is done by defining “fixed zones” before the generative evolution starts. After, a spline is created for organizing “neighbourhoods”. Several neighbourhoods are inserted along a spline, allowing for the boundaries of each one to chance when the centre is moved. Edges are selected one by one to generate a series of spaces (in this case meeting rooms) and arrange elements (in this case desks) in the rest of the neighbourhoods. By varying the input parameters, you can generate thousands of design options. Evolve: The goals have to be solved in the fastest possible manner. This stage is simply the representation of each goal as an algorithm that evaluates the possible outcomes. (Fig. 15) Evaluate: At this stage the process is automated using the previously created algorithms. These will design thousands of project iterations that meet the client specified goals. PRE-GD

POST-GD

GD Evolve

Generate

Data Constraints and requirements

Evaluate

Select Manual design refinement

Fig. 16 Generative design for architecture workflow

Generative Design: The creation of a new architect

Explore: The iterations are compared by the team and the very best are chosen. Afterwards, the results are presented and reviewed with the stakeholders, at which point the final design is selected, refined and constructed. The project was well received and the technology will be used in the development of the generative design software that will be commercialised by Autodesk. Benjamin Allan concluded with the next phrase: “...I just want you to know, that the generative design for us ,in this project ,is not just about efficiency. It is not just about a simple kind of optimization. But, it’s also about creativity ... it’s about augmenting human intuition and creativity for this process...”

Fig. 17 Torronto MaRS Builing; The location of the Autodesk Offices done by The Living

The idea that the process of generative design can offer a creative output is confusing. Moreover, what can creativity be consider to represent? In the simplest and most technical manner creativity can be recognized a result that is novel and productive. (Shelley H. Carson, Jordan B. Peterson and Daniel M. Higgins, 2005) A solution can be categorised as more creative than another. Psychology considers creativity as a scale not as a characteristic. Having this information we can further decide if a building is creative, but can generative design offer a creative solution?

Fig. 18 Floor plan of the Toronto offices

Generative Design: The creation of a new architect

2018

Sebastian Thrun

2018

2017

2016

2016 Fully Automated Luxury Communism

2015

NESTA Automation Study

2014

2013

2015

2014

Accelerationist Politics

2013

2012

2011

2010

2009

2008

David Copeâ&#x20AC;&#x2122;s Music Algorithm

2008

2007

2006

2005

University of Toronto Creativity Study

2005

C.3/// Chapter 3

As any new technology that is complex in its nature, generative design is overestimeated by architects. This chapter answeres to questions as: Is softare capable of simulating human traits such as creativity? Is artificial intelligence able to replace architects?

Generative Design: The creation of a new architect

0.5

0.6

0.7

0.8

0.9

0.5

0.6

0.7

0.8

0.9

Creative probability of occupations Artists Actors, entertainers and presenters Architects Chartered architectural technologists Public relations professionals Journalists, newspaper and periodical editors Actuaries, economists and statisticians Decorators and finishers Civil engineers Software development professionals Engineering professionals n.e.c. Quantity surveyors Quality control and planning engineers Electrical engineers Conservation professionals Environment professionals Chemical and biological scientists

0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

1.0

Visual Arts Music Creative Writing Dance Drama Architecture Humor Scientific Discovery Invention Culinary

Dystopia or Utopia?

Figure 19 Creative probability of occupations from the “Creativity vs Robots” Study

Generative design is competent and robust as a design method. This approach in design offer possibilities to improve the work-flow of an architect in terms of efficiency and accuracy. This begs the question, if artificial intelligence can design buildings what will this mean for an architect in the next ten years in terms of job opportunity? Will we become computer technicians that specialize in building information? What is our advantage as human architects against artificial intelligence? Job loss is a cause of concern. Rightfully so, as it brings financial loss and stress. It is hard for an industry to adapt to change and there will always be people that oppose the new, especially in the A.E.C. (Architecture, engineering and construction) Industry. (Allan, 2016) Movies misinformed the population and made artificial intelligence into a popular caricature. The Hollywood film, “I, Robot” presents a dystopian artificial intelligence society. Between the two leading characters there was the following dialogue:

Detective Del Spooner :

Robots don’t feel fear. They don’t feel anything. They don’t get hungry, they don’t sleep.

Sonny :

I do. I have even had dreams.

Detective Del Spooner :

Human beings have dreams. Even dogs have dreams, but not you. You are just a machine, an imitation of life. Can a robot write a symphony? Can a robot turn this [shows paper] into a beautiful masterpiece?

Sonny :

Can you?

Figure 20 Creativity score of archtecture in comparison with other creative domains

I, robot (Proyas, 2004)

So, artificial intelligence cannot write music or draw. The reality is different now; Composer David Cope, has been writing music using neural networks for 30 years. He invented a software that is capable of composing new symphonies (Recombinant Inc., 2008) and as stated by slate.com: “…he long ago reached the point where most people can’t tell the difference between real Bach and the Bach-like compositions his computer can produce… Audiences have been moved to tears by melodies created by algorithms”. (Wilson, 2010) Patrick Tresset is a London based artist who developed software and robots to prove environmental artificial intelligence. Tresset says that his robots “...are evocations of humanness”. (Tresset, 2017)

Generative Design: The creation of a new architect

Workforce Replacement

Figure 21 Patrick Tresset’s Human Study # 1: Paul 2 robot sketching a portrait starting from a live model

In both cases, the software appears to simulate creativity. But is this a true simulation or it is an imitation, and what does this mean for architecture? Architecture has been classified as a creative industry in a 2005 survey conducted by Harvard University and the University of Toronto (Shelley H. Carson, Jordan B. Peterson and Daniel M. Higgins, 2005) Moreover, NESTA (National Endowment for Science, Technology and the Arts) conducted a separate study on the possibility of automating certain jobs from the United Kingdom. They concluded that the main characteristic that are difficult to automate is creativity. Therefore, architecture has a low chance of becoming fully automated. (Hasan, B., Carl, F. and Michael, 2015) But, automation will occur since the process has already been started by Autodesk. Bill Allan considers that: “…form is subjective and there’s creativity involved” but “… there are opportunities where algorithms can mimic human behaviour and processes that we use in design.” (Allan, 2016) Viewing the problem from an other perspective is Aaron Bastani. He is a journalist at Novara Media, that proposes the idea of fully automated luxury communism, “…a vision of a utopia where people work less and live more.” This concept is simply the automation of all human working processes by robotics or artificial intelligence means. (Bastani, 2015) He believed that replacing people is key to a more stable economic system. The sum of his ideas can be theoretically considered as a branch of the object-oriented ontology. These concepts are published on the Novara Media website and attract critics and followers, but his ideas are not new. Alex Williams and Nick Srnicek published in 2013 an article called “#ACCELERATE MANIFESTO for an Accelerationist Politics”. (Williams and Srnicek, 2013) In their own words “Accelerationism pushes towards a future that is more modern, an alternative modernity that neoliberalism is inherently unable to generate”. (Williams and Srnicek, 2013) Both ideas expect a similar outcome, but creating “forms of class power” and “constructing wide-scale media reform” do not seem to provide the futuristic utopia that they promised. Both seek advancements in technology and automation as their final goal, both see this as the only way to succeed. Finally, the opinion of Sebastian Thrun, a specialist in neural networks that used deep learning tools to teach computers in Googles’ driverless cars. Is that: “…computers can simulate specialized human tasks…” and they are more capable than ever of using the neural networks method of deep learning. (Williams and Srnicek, 2013) Even if the capacities of computers in doing repetitive tasks at a high rate is remarkable, they really fall behind at managing new unknown tasks. From his experience, Sebastian mentions: “… as an artificial intelligence person, I have not seen a real progress on human creativity…” artificial intelligence is simply “…a technology that helps us do repetitive things”. (Williams and Srnicek, 2013)

Generative Design: The creation of a new architect

E./// Experiments

“To develop any design proposal so that it can become architecture requires knowledge of aesthetics, siting, function, structures, mechanical systems, graphic conventions, and perhaps even “the theory of the heavens” “ (Cuff, 1992)

Even if the description given by Cuff can not be considered for all architects, it is evident that architecture has undergone immense changes since 1991. It can be successfully argued that now an architect needs a larger set of skills to become valuable in a firm. This part contains two separate self developed experiments that allowed me to accelerate my design development and representational skills using generative design and robotics as a main driver.

Generative Design: The creation of a new architect

Voronoid Structure Flat Arrangements

Experiment Name

Voronoid structure flat arrangements based on spatial syntax principles

Aim

Create a algorithm that generates a large series of iterations that respond to given data

Inputs

Bounding space, room designation (i.e.. kitchen, Bedroom), connections between rooms, desired area, seed value

Outputs

Room arrangements, integration value, ideal door positioning, wall line, 3D model

Used Software

Rhinoceros 3D (graphical representation), Grasshopper (code creation), Space Syntax (external code library)

Figure 22 The Self- Developed algorithm result in 3D

Motivation Architects face repetitive tasks in their work. It was argued that use of computers to solve repetitive tasks would be achievable, but the advantages would be overshadowed by the necessity of collecting and managing the required data. (Reynolds, 2014)

â&#x20AC;&#x153;It has been frequently argued that computerized equipment could free man from the soul-destroying, routine, backbreaking tasks to engage in more creative work. Anybody who looks at highly automated factory must surely question whether this is, in fact, soâ&#x20AC;? M.J. Cooley

The opposite was proved in the experiment. Generative design was used to solve the The problem of designing a large range of flat arrangements.

Generative Design: The creation of a new architect

Seed: 1.0

Seed: 1.1

Seed: 1.2

Seed: 1.3

Seed: 1.4

Seed: 1.5

Seed: 1.6

Seed: 1.7

Seed: 1.8

Area

ETR

2 0

a b c d e f Legend a. Coridor b. Master Bedroom c. Living Room d. Master Toilet e. Storage f. Toilet g. Entrance h. Bedroom i. Kitchen

g h i h

ITG & CTR a

1.2 1.0 0.8 0.6 0.4 0.2

d f

10 8 6 4 2

d f

Concept

Figure 23 The representation of the seed values from 1.0 to 1.8.

The algorithm fills a given space with a voronoid 2D structure and a walking path, that are afterwards converted into 3D walls, doors and spaces. Space Syntax Theory is the core of the algorithm. From an analytical point of view, it provides a comprehensive and consistent framework for understanding spatial arrangements and their likely human effects, which we can term as social performance of buildings. (nr) The algorithm space syntax theory has was used to generate the value of integration, Difference Factor, control and choice. Galapagos was used to automate the process. The next list describes the results given by the algorithm: 1.Integration (Hillier and Hanson, 1984) decides how likely is for a space to be private or communal. (Nourian, Rezvani and Sariyildiz, 2013) 2.Difference Factor (Hanson, 1998) “...indicates how differentiated the spaces are within a configuration” (Nourian, Rezvani and Sariyildiz, 2013) 3.Control (Hillier and Hanson, 1984; Hillier et al., 1987) “...intuitively indicates how strongly a vertex in a graph (a space in a configuration) is linked to other points in a superior manner...” (Nourian, Rezvani and Sariyildiz, 2013) 4.Choice (Freeman, 1977) or “...Betweenness is a measure of importance of a node within a configuration. That literally tells how many times a node happens to be in the shortest paths between all other nodes.” (Nourian, Rezvani and Sariyildiz, 2013) Experiment conclusion The experiment allowed the creation of a large iteration list. It can be considered that the backstage coding and node programming can be time consuming in comparison with the traditional techniques. Where this method proves to be useful is its re-usability. It is possible to reuse the algorithm to design as many arrangements as needed without the having to recode anything. The single change that would be required is to modify the input data. Moreover, any architect can use the algorithm to generate the same results with minimal training.

Generative Design: The creation of a new architect

R50 R35

113mm

Support

6 2

3 4

Carriage 1. 2. 3. 4. 5. 6. 7.

Servo Motor Helical Extension Spring Pen Tensioner Mechanism LED Lights Metalic Pins/ Bolts Spring to Rope Connector Pen Push Mechanism

11 12 13

1. 2. 3. 4. 5. 6. 7.

5 6

8. 9. 10. 11. 12. 13. 14. 15.

Nema 17 Stepper Spring Holder Hot Air Exhaust Spring Holder Threaded Rod Tensioner Mechanism 1 Threaded Rod Threaded Rod Tensioner Mechanism 2 Base Support Fan Case 40x40 mm Fan Threaded Rod Fixing Support Metalic Pins/ Bolts Tensioner Helical Extension Spring Tensioner/ Spring Holder

The Architectural Turring Test

Figure 24 Diagran of the device parts requiered to build the penplotter

Experiment Name

Architectural Turring Test

Aim

Automation of hand drawing representations using a pen plotting system

Inputs

Bounding space, room designation (i.e.. kitchen, Bedroom), connections between rooms, desired area, seed value

Outputs

Room arrangements

Used Software

Motivation

Chatbot: A computer program designed to simulate conversation with human users, especially over the Internet.

In his 1950 paper “Computing , Machinery and Intelligence“ Allan Turring proposed a test to determine if a computer posses human-like intelligence. (Turring, 1950) The test has been known since as the Turring Test and is the core of the Loebner Prize competition for Chat-bot AI. Patrick Tresset was inspirational with his 2015 exhibition ”Human Traits“. (Tresset, 2017) His concept of robots that use environmental AI to draw portraits and still natures raises the question if this process can be adapted to architectural use and if so what advantages could it bring?

Pen Plotter: a system As an “old novelty“ hand drawn representations have become a sign of that uses a pen for representation/ drawing professionalism in landscape and architecture practices. The experiment considers the use of an automatic drawing system that would achieve the same results as a human draughtsman. I personally consider it as a way to understand if this technology offers an alternative to human labour or completely replaces it. Can automation be achieved by an architect for an architect and should it become common teaching. Development The experiment required a large skill set for it to be achieved. Classified on categories the research was focused mainly on the folloing areas:

Generative Design: The creation of a new architect

servo 3

servo 4

plotting area

Figure 26

Device Plotting

servo 2

x servo 1

Figure 25 Diagran showing the working concept of the pen plotter.

Stage 1: Training I got in touch with a technical firm from Romania called EcoTech. Andreea Luca provided training and advice in the firmâ&#x20AC;&#x2122;s use of software and design creation. I learned at an advanced user level: engineering industry standard software such as Solidworks, Autodesk Inventor, Fusion 360 and NetFab. Stage 2: Device Design A algorithmic function list was made to sum up the tasks the device needed to fulfil. Using Fusion 360 I finalized the device model. Stage 3: Prototyping With the help of Andreea Luca different materials were tested for the construction of the device. Different iterations were tested using galvanized steel and aluminium sheet metal. Stage 3: Electrical An electrical diagram was designed using the Design given requirements. Stage 4: Finishing Components were assembled and tested.

Experiment The experiment consists in the comparison of the results drawn by a human and the results drawn by the device over the same space division task. I used the algorithm from the last experiment to generate a space division scheme and I sent the task to three other architecture students. I compared the four designs (Figure 26-28) and concluded that the results given by the device reflect a understanding of the space arrangements. The design results were backed up by the logics and research of the former experiment. The solution given by the students were not nearly as aesthetic. The automation of the drawing process using this machine can be considered highly effective.

Generative Design: The creation of a new architect

C./// Conclusion

Generative Design: The creation of a new architect

Chapter 1 The Architecture Industry has evolved consistently to increase profits and lower expenses. Technology has been the driving factor that increased competition between practices. This is evident in the use of AutoCAD by 1980’s practices the use of Maya by Zaha Hadid Architects and the use of CATIA by Frank Gehry, LPP. I believe that the use of the generative design approach is one step in the same direction. For the Architectural, Engineering and Construction Industry, generative design lowers the design time and costs of construction. As for the possibility of failure, the scenario from the 1960s, that resulted in the abolishment of “computational architecture” will certainly not be repeated. Advancements in computing power, simulation technology and investments in technology have created a sound foundation for generative design to go mainstream. Moreover, the marketing campaigns managed by Autodesk have certainly helped push the acceptance of such technology. The discovery of the 10% rule mark in social spread of ideas from University of Rensselaer Polytechnic Institute. Moreover, the larger firms already have been using parts of generative design concepts in forms of automation (Dr.-Ing. Milos Dimcic at Programming Architecture), parametricism (Zaha Hadid Architects, Bjarke Ingles Group), or proceduralism (Michael Hansmeyer). Chapter 2 Bill Allan, from EvoLabs, confirms the emergence of generative design as part of the Accelerated growth in the Architectural, Engineering and Construction Industry. The Autodesk Mars Offices from Toronto offer a view into the capacities of the generative method. The construction is an example of the future capacities of their software package: Autodesk Generative Design. This will offer the capacity for an architect to use generative design without the need of programming or node coding. This will certainly make this technology more approachable to architects. Chapter 3 I personally consider that concepts by Alex Williams and Nick Srnicek’s “#ACCELERATE MANIFESTO for an Accelerationist Politics” and Aarron Bastani’s “Fully automated luxury communism” serve as a reminder of a dystopian future that could result from automation. The possibility of fully automating architecture as a result of generative design has been considered in this dissertation, but it is improbable. The inability of computers to recreate creative characteristics has been given as a main reason both by the Harvard and nesta study. Sebastian Thrun, the main researcher of artificial intelligence, completely refuses the possibility of simulating human-like creativity as the concept “It’s totally in the infancy.” Architects will not be replaced and will not lose their jobs due to generative design.

Experiments Even if Autodesk Generative Design has not been released at the moment of writing this dissertation, I consider that my experiments provided evidence to support the use of the generative approach in design related tasks. I consider both automation of the drawing process (The Architectural Turring Test) and design processes (Voronoid structure flat arrangements based on spatial syntax principles) to be equally valuable skills in an architect’s toolbox. If used accordingly they increase productivity. On the other side, even if it is capable, generative design should not be considered a blanket method for all design tasks. It is simply a tool that serves the purpose given by the programmer/ architect. It proved to be useful in extensively repetitive tasks. As the task was to generate a large range of flat configurations. It was easier to program the algorithm for the creation of 1000 iteration than manually modelling them. Certainly, if only 1-10 flat variants were needed, manual modelling would have been a more time efficient approach. But, generative design has another advantage. The code can be later reused in another project with other data. This would reduce the time needed for programming by around 50-80%. This is a reason why I believe that programming will become a desirable skill in architecture. The second experiment informed me on the complexity of robotics, physical automation and generative art in architecture. The drawings done using pen plotting can be considered an efficient representational alternative for traditional drawing, but the main reason for using this device was to increase my understanding regarding automation in the Industry. Future In conclusion, generative design will be the main new technology in architecture for years to come. Progressive, and commercial firms will certainly invest in the concept due to the desirable advantages of lowering design costs and time. Smaller firms will approach the idea to remain competitive. This will influence the necessity of certain architects to add a new column in their CV’s summing up their generative design capabilities. Most probably, we will see new architects that will have as main goal the automation of certain parts of construction, design. This “will empower” the architect by eliminating repetitive tasks and allow him to “…turn…” his” … creativity into action”. TED (2018)

Generative Design: The creation of a new architect

A./// Appendix

Generative Design: The creation of a new architect

References

ACADIA (2011) Nate Holland’s Association for Computer Aided Design in Architecture Presentation, [online] Available at: https://vimeo.com/22549800 (Accessed 22 December 2017). Alan, M. Turing. „Computing Machinery and Intelligence. Mind, 59(236) (1950) Allan, B. (2016) The Future of BIM Will Not Be BIM—And It’s Coming Faster Than You Think, Presentation, [online] Available at: http://au.autodesk.com/auonline/classes-on-demand/class-catalog/2016/revit/it22329#chapter=0 (Accessed 23 December 2017). Autodesk (2014) Project Dreamcatcher, Autodesk Research, [online] Available at: https://autodeskresearch.com/projects/dreamcatcher (Accessed 22 December 2017). Autodesk (2017) Generative Design at Airbus, Autodesk.com, [online] Available at: https://www.autodesk.com/customer-stories/airbus (Accessed 26 December 2017). Bastani, A. (2015) Fully Automated Luxury Communism | Novara Media, Novara Media, [online] Available at: http://novaramedia.com/2015/06/19/fullyautomated-luxury-communism/ (Accessed 13 January 2018). Bentley, P. and Creative evolutionary systems, 1st ed, San Diego, Academic Press. Corne, D. (2002) Bruton, D. and Digital design. 4th ed. London: Berg. Radford, A. (2012) Cross, N. (1977) The Automated Architect (Research In Planning And Design). England: Pion Limited. Cuff, (1992) Architecture: The Story of Practice, 1st ed, Cambridge, MIT Press. Davis, D. (2013) Modelled on Software Engineering: Flexible Parametric Models in the Practice of Architecture, Graduate, RMIT University. Daniel Davis (2015) Why Architects Can’t Be Automated, [online] Available at: http:// www.architectmagazine.com/technology/why-architects-cant-beautomated_o (Accessed 21 December 2017). Das, S., Integrated Spatial-Structural Optimization in the Conceptual Design Zolfagharian, S. and Stage of Project, In eCAADe Education and Research in Computer Haymaker, J. (2016) Aided Architectural Design in Europe, pp. 117-126, [online] Available at: https://autodeskresearch.com/publications/integrated-spatialstructural-optimization-conceptual-design-stage-project (Accessed 22 December 2017). Dassault Systèmes Frank Gehry on Dassault Systèmes and Architecture. [video] Available (2010) at: https://www.youtube.com/watch?v=-7n0XVg7nrw [Accessed 19 Dec. 2017].

Generative Design: The creation of a new architect

DeMarco, G. (2011) Scientists Discover Tipping Point for the Spread of Ideas, Rensselaer Polytechnic Institute News, [online] Available at: https://news.rpi.edu/ luwakkey/2902 (Accessed 25 December 2017). Granovetter, M. Threshold Models of Collective Behavior, American Journal of (1978) Sociology, 83(6), pp. 1420-1443. Groover, M. (2010) Fundamentals of Modern Manufacturing. 4th ed. New York: John Wiley & Sons, p.887 Hanson, J. (2011) Decoding Homes and Houses, Cambridge, GBR, Cambridge University Press. Hasan, B., Carl, F. Creativity Vs Robots. Nesta and Michael, O. (2015) Hiller, B., Burdett, R., Hiller, B., Burdett, R., Peponis, J. and Penn, A. (1987) Creating Life: Peponis, J. and Penn, Or, Does Architecture Determine Anything?, Architecture et A. (1987) Comportement/Architecture and Behaviour, 3(3), pp. 233-250. Hillier, B. and The social logic of space, Cambridge University Press. Hanson, J. (1984) Holland, N. (2011) Project Teaser, [online] Available at: https://vimeo.com/23061345 (Accessed 20 December 2017). Keller, S. (2006) Fenland Tech: Architectural Science in Postwar Cambridge, Grey Room, 23, pp. 40-65. Kind Code (2007) Method and system for automated design, US. Harsuvanakit, A. Elbo Chair- Generated in Project Dreamcatcher, Made with Fusion 360, (2017) Autodsk Gallery, [online] Available at: https://gallery.autodesk.com/ fusion360/projects/elbo-chair--generated-in-project-dreamcatchermade-with-fusion-360 (Accessed 23 December 2017). Mathews, B. (2017) Generative Design Opens a New Era for Products Innovation., Presentation. McPhee, A. et al. What is LOD, is it useful or just another pointless BIM deliverable?, (2013) Linked-In Discussion: http://www.linkedin.com/groups/What-is-LOD-isit-98421.S.218542523?view=&srchtype=discussedNews&gid=98421&ite m=218542523&type=member&tr k=eml-%E2%80%A61/20 Dimcic, M. (2011) Programming Architecture - Automating Building Industry, Programmingarchitecture.com, [online] Available at: http://www. programmingarchitecture.com (Accessed 13 January 2018). Nourian, P., Rezvani, Designing with Space Syntax A configurative approach to S. and Sariyildiz, S. architectural layout, proposing a computational methodology, In (2013) 31st eCAADe Conference. Poincaré, H. (1885) Sur l’équilibre d’une masse fluide animée d’un mouvement de rotation, Acta Mathematica, 7(1), pp. 259-380, [online] Available at: https:// link.springer.com/article/10.1007%2FBF02402204?LI=true (Accessed 16 December 2017).

Princeton School of Patrik Shumacher and Michael Hansmeyer - F’14 SoA Lecture Architecture (2014) Series, “The New How” (112414). [video] Available at: https://vimeo. com/112854141 [Accessed 20 Dec. 2017]. Proyas, A. (2004) I, Robot, Hollywoood, Twentieth Century Fox. Recombinant Inc. Recombinant music composition algorithm and method of using the (2008) same, US. Schumacher, P. Parametricism: A New Global Style for Architecture and Urban Design, (2009) Architectural Design, 79(4), pp. 14-23. Sennott, S. (2004) Encyclopedia of 20th century architecture, New York, Fitzroy Dearborn. Shah, D. and Parametric and Feature-Based CAD/CAM. New York: John Wiley & Sons Mäntylä, M. (1995) Inc, p.133. Shea, K., Aish, R. Towards integrated performance-driven generative design tools, and Gourtovaia, M. Automation in Construction, 14(2), pp. 253-264. (2005) Shelley, H. Carson, Jordan, B. Peterson and Daniel, M. Higgins. (2005)

Reliability, Validity, and Factor Structure of the Creative Achievement Questionnaire. Available at: https://www.researchgate.net/ publication/234822027_Reliability_Validity_and_Factor_Structure_of_ the_Creative_Achievement_Questionnaire (Acessed: 08 November 2017).

Soddu, C. (1994) Generative Design, Art and Science, Soddu.it, [online] Available at: http://www.soddu.it (Accessed 26 December 2017). Soddu, C. (2017) Designing Generative Art. Mosaic concept, Creativity, and Idea in Generative Design, In 20th Generative Art Conference GA2017, Italy, Generative Art Conference, [online] Available at: https://www. generativeart.com (Accessed 26 December 2017). Stanislaw, U. (1958) Tribute to John von Neumann, Bulletin of the American Mathematical Society, 64, [online] Available at: https://docs.google.com/file/d/0B5-JeCa2Z7hbWcxTGsyU09HSTg/edit?pli=1 (Accessed 21 December 2017). Stocking, A. (2011) Generative Design Is Changing the Face of Architecture, Cadalyst, [online] Available at: http://www.cadalyst.com/cad/building-design/ generative-design-is-changing-face-architecture-12948 (Accessed 26 December 2017). TED (2018) What AI is - and isn’t, [online] Available at: https://www.ted.com/ talks/sebastian_thrun_and_chris_anderson_the_new_generation_of_ computers_is_programming_itself (Accessed 13 January 2018). The Living (2018) The Living, Thelivingnewyork.com, [online] Available at: http://www. thelivingnewyork.com (Accessed 13 January 2018). Tresset, P. (2017) Patrick Tresset, Patricktresset.com, [online] Available at: http:// patricktresset.com/new/ (Accessed 21 December 2017). Reynolds, R. (2014) Computer Methods for Architects, Burlington, Elsevier Science. Van Bruggen, C. Frank O. Gehry: Guggenheim Museum Bilbao. 1st ed. Bilbao: (1997) Guggenheim Museum Publications, pp.135-138.

Generative Design: The creation of a new architect

Yan, H. and Demian, Benefits and barriers of building information modelling. IN: Ren, A., P. (2008) Ma, Z. and Lu, X. Proceedings of the 12th International Conference on Computing in Civil and Building Engineering (ICCCBE XII) & 2008 International Conference on Information Technology in Construction (INCITE 2008), Beijing, China, 16th-18th October 2008. Available At: https://dspace.lboro.ac.uk/2134/23773 Williams, A. and #ACCELERATE MANIFESTO for an Accelerationist Politics, Critical Srnicek, N. (2013) Legal Thinking, [online] Available at: http://criticallegalthinking. com/2013/05/14/accelerate-manifesto-for-an-accelerationist-politics/ (Accessed 13 January 2018). Wilson, C. (2010) Iâ&#x20AC;&#x2122;ll Be Bach, Slate Magazine, [online] Available at: http://www.slate. com/articles/arts/music_box/2010/05/ill_be_bach.html (Accessed 21 December 2017). Xie, J., Sreenivasan, Social consensus through the influence of committed minorities, S., Korniss, G., Physical Review E, 84(1). Zhang, W., Lim, C. and Szymanski, B. (2011)

List of Illustrations NOTE: ALL IMAGES AND DIAGRAMS WERE PRODUCED SOLELY BY THE AUTHOR UNLESS OTHERWISE NOTED.

Figure 2 Soddu, C. (2013). Generated NYC Identity. [image] Available at: http://www.generativedesign.com/arch_1/NYC/index.html [Accessed 12 Jan. 2018]. Figure 3 Bohnacker, H. (2009). Process for creating generative design. [image] Available at: http://www.generative-gestaltung.de [Accessed 12 Jan. 2018]. Figure 4 375 Park Avenue Seagram Building (n.d.). Seagram Building. [image] Available at: http://375parkavenue.com/Building [Accessed 12 Jan. 2018]. Figure 5 Ghrabi, A. (2011). [image] Available at: https://www.flickr.com/photos/nystagmus/8562748374/sizes/h/ [Accessed 12 Jan. 2018]. Figure 6 Sutherland, I. (1962). Ivan Sutherland using Sketchpad in 1962. [image] Available at: http://history-computer.com/ModernComputer/Software/Sketchpad.html [Accessed 12 Jan. 2018]. Figure 7 Goergen, E. (2006). Guggenheim Museum Bilbao. [image] Available at: https:// en.wikipedia.org/wiki/Guggenheim_Museum_Bilbao#/media/File:GuggenheimBilbao.jpg [Accessed 12 Jan. 2018]. Figure 9 Hansmeyer, M. (2008). Platonic Solids. [image] Available at: http://www.michael-hansmeyer.com/projects/platonic_solids.html?screenSize=1&color=0#2 [Accessed 12 Jan. 2018]. Figure 10 Dezeen (2013). Interior view of the Bao`An Airport. [image] Available at: https:// www.dezeen.com/2013/11/26/studio-fuksas-terminal-3-shenzhen-baoan-international-airport/ [Accessed 12 Jan. 2018]. Figure 11 Dimcic, M. (n.d.). Automating the entire building process. [image] Available at: http://www.programmingarchitecture.com/ [Accessed 12 Jan. 2018]. Figure 12 Autodesk (2016). The accelerate growth in the AEC Industry. [image] Available at: http://au.autodesk.com/au-online/classes-on-demand/class-catalog/2016/revit/ it22329#chapter=0 [Accessed 12 Jan. 2018]. Figure 13 Autodesk (2016). Data management in architecture. [image] Available at: http:// au.autodesk.com/au-online/classes-on-demand/class-catalog/2016/revit/ it22329#chapter=0 [Accessed 12 Jan. 2018]. Figure 14 Amazon Web Services (2017). AWS re:Invent 2017. [video] Available at: https://www. youtube.com/watch?v=A31A8KDC9S4 [Accessed 12 Jan. 2018].

Generative Design: The creation of a new architect

Figure 15 The Living (2018) The Living, Thelivingnewyork.com, [video] Available at: http:// www.thelivingnewyork.com (Accessed 13 January 2018). Figure 16 Autodesk (2017). Generative design for the architecture workflow. [image] Available at: http://au.autodesk.com/au-online/classes-on-demand/class-catalog/classes/ year-2017/dynamo-studio/as124721#chapter=0 [Accessed 12 Jan. 2018]. Figure 17 Autodesk (2015). Autodesk Toronto is Moving to MaRS. [image] Available at: http:// inthefold.autodesk.com/in_the_fold/2015/11/autodesk-toronto-is-moving-to-mars. html [Accessed 12 Jan. 2018]. Figure 18 The Living (2016). Autodesk Mars Torronto office plan. [image] Available at: https:// vimeo.com/193915345 [Accessed 12 Jan. 2018]. Figure 19 Hasan, B., Carl, F. and Michael, O. (2015) Creativity Vs Robots. Nesta Figure Shelley, H. Carson, Jordan, B. Peterson and Daniel, M. Higgins. (2005).Reliability, Va20 lidity, and Factor Structure of the Creative Achievement Questionnaire. Available at: https://www.researchgate.net/publication/234822027_Reliability_Validity_and_Factor_Structure_of_the_Creative_Achievement_Questionnaire (Acessed: 08 November 2017). Figure 21 Horak, S. (2012). Patrick Tressetâ&#x20AC;&#x2122;s Human Study # 1: Paul 2 robot sketching a portrait starting from a live model. [image] Available at: http://patricktresset.com/various/ book_exhibitions_lowres.pdf [Accessed 12 Jan. 2018].

Generative Design: The creation of a new architect