4. Research Methodology

from THE IMPACT OF AI IN ARCHITECTURAL DESIGN

2. Literature Review

controlled by input parameters set by the designer. Hence, the algorithms that makes this system perform functions can be thought of as co-designers. (Radziszewski, Cudzik, & Kacper, 2018)

This model has been experimented by on column designs, the AI system was trained on detailed configurations of the Roman Corinthian order capitals. Automated design variations were created by tiny deformations of the initial design, the ability to deform the model was set by the designers as a constraint for the system to work under. The successfully trained neural network was able to produce both purposeful and random design variations to the new capital forms. “Neural networks, that are less predictable, intuitive, resembling human-like decision-making process, became a way of data computing that is able to extend the set of architectural computational design tools”. (Radziszewski, Cudzik, & Kacper, 2018)

Fig 11. Capitals automatically designed with machine learning

4. RESEARCH METHODOLOGY

4.1. Case Studies

4.1.1. Case Study 1 - New Delve Generative Design Tool

Designers: Sidewalk Labs Description: The use of different platforms and different methods of data collection by architects, engineers and business department leads to the fragmentation of data. This makes the process inefficient and sometimes even expensive, limiting variation in design possibilities. (Hickman, 2020)

Fig 12. Every aspect of the project is taken into consideration

Delve is a generative design tool that uses machine learning to bring all variables and data into one platform, providing several design possibilities along with informing designers about the goals they have initially set and to what extent have to satisfied those criteria. This system is able to complement each step of the design process, from site selection to planning and from approvals to financing. (Hickman, 2020)

Fig 13. Baseline design compared with Delve generated design

How does it work: The developer shares their priorities, such as minimizing costs, increasing access, daylight, etc. Then the constraints are set, such as area and how these areas are divided among residential, commercial, retail, etc. Delve then incorporates all the data and produces designs. Information about each design option is displayed, making it easier to choose the best design.

The designers can easily go back and make changes to the constraints, Delve 4.1.2.

being a computer system is quick to iterate the design based on the new set parameters and produce desired results. (Hickman, 2020)

Fig 14. Connectivity and accessibility suggestions are provided by the system

The machine learns by monitoring each selection made by the designer and storing the best selected results which helps in producing better results in the

future. Case Study 2 - AI City

Designer: BIG architects Location: Chongqing, China

Fig 15. AI city integrates the convex curve of lakes and the concave curves of mountains that surround the site

Description: Terminus AI City Operating System will be responsible for operating every function of this AI city. "Cloud Valley is envisioned as a city where people, technology and nature thrive together – with spaces designed for all types of life: human life, plant life, animal life and even artificial life." Says Ingels (Harrouk, 2020)

Robots performing in the cities courtyards, self-driving cars and other automated systems will make this a place where humans intelligence and artificial intelligence live and work in harmony. Traffic jams will be decreased by the use of e-bikes and self- driven cars. (Harrouk, 2020)

Fig 16. Performance by robots in the courtyard

Terminus Group, the software designers for this system describes the working of this building and a typical day in the life of an inhabitant of the AI City, in the following quotes.

"As sunlight hits the houses, bedroom windows adjust their opacity to allow the natural light to wake sleepy residents,"

"Once the light has filled the room, an AI virtual housekeeper named Titan selects your breakfast, matches your outfit with the weather, and presents a full schedule of your day using Terminus Group's smart transportation solution,"

"After breakfast, you step into your intelligent, fully-automated vehicle, and begin your intercity commute browsing global market news – recommended by an algorithm, of course!" (Harrouk, 2020)

4.1.3.

Fig 17. The city is operated by AI system

This is how AI technology will be integrated into our daily lives. The system will collect data and this data will be used to improve various aspects of city planning, security and connectivity. AI systems will constantly analyze the data which leads to constant upgrades in the operations of this city. (Harrouk, 2020)

Case Study 3 - Recoding Post-War Syria

Designers: Reparameterize Studio Location: Damascus, Syria Description: The ancient city of Damascus is destroyed by wars and conflicts, precious architecture, houses and entire communities were affected. Reparameterize Studio is looking to re-generate or re-code the city. Documentation, data visualization, statistics and analytics will be the backbone to re-generate the city of Damascus into a smart city. (Studio, 2020)

Fig 18. Scanned 3D model of Damascus

Parameters such as heritage, history, culture, climate, environment, economy, transportation, typology, public health, population, services, society, politics, religion, damaged buildings and building codes will be fed into the system. (Studio, 2020)

Detected damaged structures are divided into: 1) Moderate damage 2) Severely damaged 3) Destroyed 4) No visible damage

Fig 19. Structural analysis model of a damaged building

Roads and walkways were damaged and interrupted by rubbles from the destruction. With the use of a 3D scanning technology, the designers were able to analyze the existing fabric, and then decide which area requires reconstruction and which are inhabitable. The cloud points that were collected, enabled the designers to create an accurate model of the city along with the structural system of existing buildings and structures. (Studio, 2020)

Data about structures that are lost and opinions about the future of Damascus were collected by local citizens and the refugees using surveys and social media platforms. This data was then entered along into the system, computerized models were created which were placed side-by-side with the cloud points that were collected from scanning the streets. The system then decided the level of reconstruction needed to each building and its structural system based on the amount about damaged incurred. (Studio, 2020)

Fig 20. Points mapped out in 3D space, used to create computer model

4.1.4.

Fig 21. Reconstructed model of damaged buildings created after assessment

All the data is synchronized using AI, and a system is developed to regenerate post-war cities. Considering sustainability, eco-systems, improving mobility and smart technology, the system is then able to guide the designer. As the destruction is uneven, the amount of construction required in a community will be suggested by the AI system. The system will also suggest in deciding the type of architecture that is required. (Studio, 2020)

Case Study Findings

Analyzing the working of systems and the ideology behind each of the case studies, some common mechanisms can be drawn. Since the general

4.1.5. 4.2.1. 4.2.2. functioning of AI is the same for every system, we can reach at a conclusion by comparing the cases.

Every designer that seeks to use the advantages of AI in their project, starts by collection data. This data is then inserted into the system. The designer will then start training the machine with the data that was inserted. Next, design constraints are set in the system which makes each system specialized in the task that it was built for. The system then trains under the set parameters, this is where AI truly start showing intelligence by learning and analyzing the data. The system then generates results from which the best results are chosen by the inbuilt algorithms, the best results are generated after the previous results are further refined. The designer then choses the best design solutions, and this choice is recorded and studies by the AI system, this process constantly upgrades the system to produce better results over time.

Case Studies Implication Scale Scope Brief

Delve

AI City

Recoding Syria General

Specific

Specific Medium Scale (City Blocks) Concept design to execution Generative design tool run by AI

Medium Scale (City Blocks) Concept design to planning City operations controlled by AI

Large Scale (Entire City) Concept design

Re-design of buildings by AI

4.2. Interview

Case Study Comparison Interview Background The interview was conducted with Dr. Basem Mohamed, an assistant architecture professor at Zayed university. He was previously an associate architecture professor at Abu Dhabi University. He was interviews because of his extensive knowledge regarding the digital and technological aspect of architecture. His answers were very useful in giving direction and weight to this research.

Highlights of the Interview

Dr. Basem Mohamed started the interview by describing AI as “Intelligence by machines”, sometimes better than humans. Stating that AI has been a part of architecture for a while, in aspects such as space layout planning, optimization and fabrication. This technology is used by some firms in the form of generative design tools, helping in quickly generating conceptual designs constrained to specific regulations set by the designer. He agrees that machines have moved from being drafting tools to design assistants.

4.3.

He suggests that current AI Technology are not suitable for small firms 4.3.1. 4.3.2.

because of the huge investments required in hardware and software. He indicates that the role of architects will become more like “system designers” rather than “building designers”, and not everyone will be able to do so. He believes that architects need to embrace the changes and architecture students should be taught coding at university.

He concludes by saying that Artificial Intelligence might not surpass human intelligence but will help in saving time by automating some design and production processes. Hence, AI will slowly but surely be implemented widely

Survey

and will become the norm. Survey Sample Area

The main goal of this survey was to recognize the awareness of AI among people from different age groups and genders. Their education background was also taken into consideration, most of the responders being university students. Around 15 people gave their response to this survey which was distributed online.

Survey Results

Given below are the results obtained from the survey conducted, the first few questions were demographic based and general AI questions.

The first question asks the participant about his/her gender, to compare the awareness of AI technology among males and females. 54% of the responses were from females and the rest 46% were from males. Then the responders chose an age group that they fall in. Of which 23% of the participants are from 15 to 20 years of age and 77% of the participants are 20 to 25 years old. And regarding education background, 70% of participants are pursuing a bachelor’s degree, high school and diploma students were each consisting 15% of the total population. When asked about familiarity with AI technology, 70% of the responses have brief understanding of AI. The percentage of responders who have heard about the term but has no idea about AI and the percentage of population who are able to create and use AI applications are exactly the same, being 15%.

70 percentage of participants were aware about the integration of AI in architecture and the rest were not aware. When asked about AI automating the design process, 30 % of the responders believes that the architectural design process cannot be automated, due to lack of computer performance. And the rest agrees that AI could automate the process. 85% of the participants believed that machines have started behaving like design assistants. And 15% of the responders believe that computers are design tool just like a pencil.

5.

6.

When asked about the ability of AI to surpass human intellect, 54% of the participants believe that it is possible, and the rest believe that it is not. 46% of responders believe that the advancement in AI could cause job shortage in the architecture industry, 38% of participants believe that the role of an architect will transform over time, but this change will not cause job shortage. And the rest of the population believes that AI might take their jobs.

Finally, 100% of the responders believe that AI technology should be taught at university. Students should be aware about the systems and the advancements in their profession.

DISCUSSION

Going through the technology behind the latest trends in architecture that are influenced by AI, and analyzing the data retrieved from the case studies and surveys, we can say that AI has having a major impact in the field of architecture. The effects of AI will continue to increase as the technology keeps advancing.

As far as automation of the design process goes, there are methods to automate certain parts of the process. And a combination of these technologies could automate a large part of design. Ways to design and recognize architectural plans has been discussed, these computer-generated plans can be extruded by available systems. Automatically placing furniture and generating openings for doors and windows. This creates the outer shell for the building, façade recognition and design systems can design facades on these 3D masses. Each building can have unique designs by the use of automated design variations in architectural elements such as column.

Like human intelligence, AI will continue to learn and evolve over time, incorporating advanced automation techniques in each step of the design process.

CONCLUSION

The integration of AI into architecture looks to reinvent and reimagine the architectural design process and framework. Inventing ways to aid the designer and automate part of the process. Starting from recognition and generation of floor plans using GNA (Generative adversarial network). This system works by color coding the portions of the plan and generating a color map. Another trained system can recognize the color mapped plans and generate an architectural plan using the data. Zoning data, building codes, and disabled design data along with other relevant information can be easily and readily accessed by an architect using an AI system. Hence the architect can focus on other important aspects of designs, as the burden to find the standards and other readily available data is shifted upon an AI system. A similar plan generator can use tree maps and tree diagrams to divide floor plans, AI can stretch the abilities of computers to generate floor plans automatically, using minimal input parameters by the designers. These generated floor plans can be extruded into 3D masses, over which designers can perform façade treatment using façade recognition and design software’s. Trained artificial neural networks are able to produce both purposeful and random variations of design, based on

input parameters. Machines which were once used as design tools and an extension of an architect’s pencil, can now be taught to generate design variations and suggest the best course of action to the designer.

Generative design, bias and architectural style are one of the major points to focus on when exploring the future of AI and architectural interaction. Turning these points into computer understandable language, to train the AI system can turn out to be a challenge. But as the system improves, the system might find ways to learn based on the architect’s inputs and system could be crated that is able to design and maintain other systems. Software creation and system development requires time and money, making it difficult to experiment with these technologies. Working with AI required deep knowledge of computers, coding and machine language, people in field not related to computer might find it difficult learn and adapt to this new technology.

7. REFERENCES

Retsin, G. (2019). Discrete architecture in the age of automation: A.D. Architectural Design, 89(2), 6-13. doi:http://dx.doi.org.adu-lib-database.idm.oclc.org/10.1002/ad.2406

Dounis, A. I. (2010). Artificial intelligence for energy conservation in buildings. Advances in Building Energy Research (ABER), 4(1), 267-299.

Huang, W., & Zheng, H. (2018). Architectural drawings recognition and generation through machine learning.

Malaeb, J., & Ma, W. AIA.(2019) https://scholar.google.ae/scholar?hl=en&as_sdt=0%2C5&as_vis=1&q=Artificial+Intellige nce+in+Architecture+Jamal+Malaeb1%2C+Professor+Wejung+Ma1%2C+2&btnG=

Cudzik, J., & Radziszewski, K. (2018). Artificial Intelligence aided architectural design. Baldwin, E. (2019, June 05). AI Creates Generative Floor Plans and Styles with Machine Learning at Harvard. Retrieved from Arch Daily: https://www.archdaily.com/918471/aicreates-generative-floor-plans-and-styles-with-machine-learning-at-harvard

Chaillou, S. (2019, Febuary 24th). AI & Architecture. Retrieved from Towards Data Science: https://towardsdatascience.com/ai-architecture-f9d78c6958e0

Marson, Fernando & Musse, Soraia. (2010). Automatic Real-Time Generation of Floor Plans Based on Squarified Treemaps Algorithm. International Journal of Computer Games Technology. 2010. 10.1155/2010/624817.

Bacharidis, K. S., Ragia, F., & Lemonia. (2020). 3D Building Façade Reconstruction Using. Geo-Information, 24.

FRANKENFIELD, & JAKE. (2020, 3 13). Artificial Intelligence. Retrieved from Investopedia:https://www.investopedia.com/terms/a/artificial-intelligence-

ai.asp#:~:text=Artificial%20intelligence%20(AI)%20refers%20to,as%20learning%20and %20problem%2Dsolving.

Harrouk, C. (2020, 9 30). BIG Designs AI CITY, an Innovation Campus Hosting Headquarters of Tech Firm in Chongqing, China. Retrieved from Arch Daily : https://www.archdaily.com/948764/big-designs-ai-city-an-innovation-campus-hostingheadquarters-of-tech-firm-in-chongqing-china

Hickman, M. (2020, 10 15). Sidewalk Labs launches Delve, a generative design tool for optimized urban development. Retrieved from The Architecture Newspaper: https://www.archpaper.com/2020/10/sidewalk-labs-launches-delvegenerative-designtool-for-optimized-urban-development/

Radziszewski, Cudzik, J., & Kacper. (2018). Artificial Intelligence Aided Architectural Design. 9.

RauppMusse, FernandoMarson, & Soraia. (2010). Automatic Real-Time Generation of Floor Plans Based on. 11.

Studio, R. (2020). Recoding Post-War Syria. Retrieved from Reparametrize Studio: http://reparametrize.com/project/recoding-post-war-syria/

Image Credits

Figure 1: Stanislas Chaillou. (2019). AI+Architecture . https://www.archdaily.com/918471/ai-creates-generative-floor-plans-and-styles-withmachine-learning-at-harvard

Fig 2, 3, 4 & 5: Huang, W., & Zheng, H. (2018). Architectural drawings recognition and generation through machine learning.

Fig 6, 7, 8, 9 & 10: Bacharidis, K. S., Ragia, F., & Lemonia. (2020). 3D Building Façade Reconstruction Using. Geo-Information, 24.

Fig 11: Radziszewski, Cudzik, J., & Kacper. (2018). Artificial Intelligence Aided Architectural Design. 9.

Fig 12, 13 & 14: FRANKENFIELD, & JAKE. (2020, 3 13). Artificial Intelligence. Retrieved from Investopedia:https://www.investopedia.com/terms/a/artificial-intelligenceai.asp#:~:text=Artificial%20intelligence%20(AI)%20refers%20to,as%20learning%20and %20problem%2Dsolving. Fig 15, 16 & 17: Harrouk, C. (2020, 9 30). BIG Designs AI CITY, an Innovation Campus Hosting Headquarters of Tech Firm in Chongqing, China. Retrieved from Arch Daily : https://www.archdaily.com/948764/big-designs-ai-city-an-innovation-campus-hostingheadquarters-of-tech-firm-in-chongqing-china

Fig 18, 19, 20 & 21: Hickman, M. (2020, 10 15). Sidewalk Labs launches Delve, a generative design tool for optimized urban development. Retrieved from The Architecture Newspaper: https://www.archpaper.com/2020/10/sidewalk-labs-launchesdelvegenerative-design-tool-for-optimized-urban-development/

APPENDIXES

Appendix 1

The following questions were asked in the interview.

1. How will you describe AI to the public in simple terms? 2. Which AI technologies are currently used in architecture industry? 3. Is the emergent AI technology ready for use by the industry? 4. Are we close to an automated architectural design process? 5. How will the role of an architect change? 6. Have machines moved from being drafting tools to design assistants? 7. Will machines in the future surpass human intelligence and design abilities? 8. Should architects be afraid and concerned about their jobs? 9. Will you recommend AI technology be taught to architecture students at University? 10. Finally, is this all a hype or reality? Should people be concerned and more aware?