Fig 1. AI+Architecture
THE IMPACT OF AI IN ARCHITECTURAL DESIGN
NAME
Faisal Ahmed
COURSE TITLE
Research Methods & Programing
INSTRUCTOR
Dr. Nilufer Ozak
DATE
06.12.2020
TABLE OF CONTENTS
List of Illustrations Abstract and Keywords 1. Introduction………………………………………………………………………………………….5 2. Literature Review……………………………………………………………………………………6 2.1. Artificial Intelligence………………………………………………………………...6 2.2. Introduction of AI into architecture………………………………………………...6 2.3. Computers, from design tools to design assistants……………………………..7 3. AI transforming the design process……………………………………………………….…....8 3.1. Architectural drawing recognition and generation……………………………....8 3.2. From plans to architectural elements……………………………………….........9 3.3. Façade Reconstruction Using Deep Learning……………………………........11 3.4. Automated design variations………………………………………………….....12 4. Research Methodology……………………………................................................................13 4.1. Case Studies…………….…………................................................................13 4.1.1. Case Study 1 - New Delve Generative Design Tool....................13 4.1.2. Case Study 2 - AI City…………....................................................15 4.1.3. Case Study 3 - Recoding Post-War Syria………………….……...17 4.1.4. Case Study Findings…….............................................................19 4.1.5. Case Study Comparison……………………..................................20 4.2. Interview 4.2.1. Interview Background…………………….…..................................20 4.2.2. Highlights of the Interview……………….…..................................20 4.3. Survey…………….…………...........................................................................21 4.3.1. Survey Sample Area.....................................................................21 4.3.2. Survey Results.............................................................................21
5. Discussion……………………………....................................................................................22 6. Conclusion……………………………....................................................................................22 7. References……………………………....................................................................................23 Appendices Appendix 1: Interview questions Appendix 2: Survey Results in Graphs
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List of Illustrations
Fig 1. AI+Architecture…………………………………….………………………………………………1 Fig 2. Workflow of a typical GAN……………………….……………………………………………….8 Fig 3. Color codes given to each part of the floor plan.………………………………………………9 Fig 4. Color map generated from floor plan……………………….…………………………………...9 Fig 5. Floor plan generated from colored map……………………….………………………………..9 Fig 6. Tree diagram (a) and related tree map (b) ……………………….…………………………..10 Fig 7. Floor plan generation process……………………….………………………………………....10 Fig 8. Walls and opening generated from a 2D floor plan……………………….………………….11 Fig 9. 3D building model and façade reconstruction, by combining image and georeferenced point data. ……………….………………………………………………………………………………12 Fig 10. Updated workflow for generating a 3D building model and utilizing AI based technology and georeferenced data……………….……………………………………………………………….12 Fig 11. Capitals automatically designed with machine learning……………….…………………..13 Fig 12. Every aspect of the project is taken into consideration……………….…………...……….14 Fig 13. Baseline design compared with Delve generated design……………….……………...….14 Fig 14. Connectivity and accessibility suggestions are provided by the system………………....15 Fig 15. AI city integrates the convex curve of lakes and the concave curves of mountains that surround the site……………….……………………………………………………………….……….15 Fig 16. Performance by robots in the courtyard……………….…………………………………….16 Fig 17. The city is operated by AI system……………….………………………………………..….17 Fig 18. Scanned 3D model of Damascus……………….………………………………………...….17 Fig 19. Structural analysis model of a damaged building……………….………………………….18 Fig 20. Points mapped out in 3D space, used to create computer model……………….…….….19 Fig 21. Reconstructed model of damaged buildings created after assessment……………….…19
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ABSTRACT This research aims to study the impact of Artificial Intelligence in the field of architecture. Focused on the influence of AI technology on the architectural design process. Starting with a general explanation of AI, moving to the advancements of AI specially in the field of architecture. The research will progress into the specifics of new and emergent technologies that improves each step of the design process, and the prospect of automating the design framework. A glance into case studies of small scale to large scale projects, which have been elevated by the intervention of AI. Description of information collected from an interview with an expert in the field. Reports of a survey which was conducted and distributed online aims to portray the understanding of AI among people of different age groups and backgrounds. It is interesting to note that, 70% of the responders of the survey believe that AI could automate the architectural design process. Discussions will be done on the rise of AI and the transformation of machines from design tools to design assistants.
KEYWORDS Artificial Intelligence, architecture, recognition, generation, training, analysis, data collection, Generative Adversarial Network, Pix2pixHD, extrude, tree maps, tree diagrams, faรงade modeling, deep learning, design variations, algorithm, regeneration, machines, systems and technologies.
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1. INTRODUCTION When people hear about Artificial Intelligence, they usually think about robots that are out there to take away our jobs and eventual take over the whole world as they evolve. But this is far from the truth. AI is a computer system that learns and grows in the field it was designed for. It can be thought of as our assistants in offices and industries, but rather than performing tasks as commanded by humans, AI is able to assist us and suggest us the best course of action. The reality is, AI will transform the way we live and the processes we follow. (FRANKENFIELD & JAKE, 2020) This research is aimed at studying the impact of Artificial Intelligence on Architectural design process. The type of equipment and systems that are needed to achieve this. Evaluating the extent of automation possible in architectural design and the implications of AI in the field of architecture, evaluating how the role of an architect will change over time (Malaeb, J., & Ma, W. AIA.2019). We know that AI can learn and perform repeated tasks, better and faster than humans, but can AI move one step forward and start analyzing and providing solutions to lower order thinking tasks and develop further into machines that solve problems that are not yet solvable by humas. AI can learn similar to how humans learn, that is by creating connections inside the brain and in the case of AI, the connections are formed in the systems neural network.(Huang, W., & Zheng, H. 2018) Though Artificial Intelligence has been developing independently over the years, its integration with several other fields has been very recent, architecture is considered to be one of the latest industries to have started learning about its implications in the field. Many of the studies relating to the integration of AI in Architecture are still in the experimental stage, but very soon AI could be taking over all walks of life, hence it is important as architects to manifest this technology and make use of its benefits. Machine learning systems currently being tested are able to generate automated floor plan (Huang, W., & Zheng, H. 2018), similarly system studied the architectural Roman Corinthian order and produce several variations in design automatically (Cudzik, J., & Radziszewski, K. 2018). This research paper will dive in the possibility of AI making connections and analyze the several deeper aspects of architecture, such as proportions, scales, psychology, thermal comfort and other sensitive data. Several cases studies will be analyzed, which will provide information about the latest AI technologies used in the field of architecture. We will also see where the technology is heading and what to expect in the future. An interview was conducted with an expert dealing with digitalization in architecture and the advancements in architecture. Data was also collected by distributing questioners to a wide range of people from university students to working professionals, this showed the extent of familiarity that people have about AI and how widespread the interest is on the topic. An interview was conducted with an architecture professor who happens to be well informed about the interaction between AI and architecture. A general description of AI and its functions, its integration into several industries and how has the industry started to function differently after the introduction of AI, will be given. Detailed analysis on the introduction of AI in the field of architecture and the implications 5|Page
that it had on the architectural design process. Evaluation of the several AI systems and techniques that are transforming architectural design and automating some parts of the process. Several research mythologies will be used to collect, analyze and describe the data. 2. LITERATURE REVIEW 2.1.
Artificial Intelligence
Artificial Intelligence works by simulating human intelligence on computer systems that are programed to think and work like humans. AI works on the principles of learning, reasoning, and perception, it mimics the functioning of human brain and makes connections between packets of information. The application of AI is vast reaching, expanding into several industries and sectors. (FRANKENFIELD & JAKE, 2020) Based on the capabilities of AI, is divided into weak and strong. Weak AI is designed to solve a specific task and its capabilities are limited to that task, for example a system designed to play chess against human opponents. On the other hand, strong AI systems has a human-like analysis capabilities, they are designed to perform more complex and complicated tasks. This system has the ability to work without human intervention, for example the system that operates self-driving cars. (FRANKENFIELD & JAKE, 2020)
2.2.
Introduction of AI into architecture
The architectures design process has been evolving ever since designers started designing. As technology improved and new design tools are invented, they are integrated in the architectural process. After the advent of Artificial Intelligence and its integration in several fields, the entire process of architectural design could be reinterpreted along with the role of architects themselves (Retsin, G. 2019). This push could prove as a great opportunity for architects to examine their current design process and make improvements in the way they work to produce even more sophisticated architecture. As the architectural production line changes and architects start tacking new and old problems, it would be great if designers can get some help in figuring out answers to complex challenges and this is again where Artificial Intelligence comes into play. In some cases, a sophisticated and well evolved AI system can generate design solution much better than humans (Dounis, 2010). Considering cases such as climate change and green designs, humans have done some progress, but the situation is getting worse and AI intervention might be the break though architects were waiting for. AI show great potential as the automated building systems that they operate on set goals such as energy efficiency, comfort, health and productivity in living spaces (Dounis, 2010). And these goals can be set and tweaked in the initial systems. As the system evolves and develops independently, more hard points and goals would be set by the AI systems which may also include points missed out by architects, combination of both would yield a more sustainable architectural design.
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As the possibility of generative green design is being discussed, it is important to shift our focus on the current advancements in generative design. Technology to recognize and generated architectural floor plans has been successfully studies and experimented. Generative adversarial network also known as GAN has made this possible (Huang, W., & Zheng, H, 2018). GAN is a framework modeled out of machine learning, it studies similar or identical characteristics and can learn or produce specific design based on data that was fed to the system. Pix2pixHD is a reworked version of GAN which learns image data in pairs and generates new images based on the input (Huang, W., & Zheng, H, 2018). Pix2pixHD was trained by assigning color codes to each aspect of an architecture plan, for example the walkway was assigned red color, bedrooms were lite green, windows were dark red, doors dark green and so on. Several architectural plans were inputted to the machine and the system was able to produce a color-coded plan. This process was then reversed, training the machine to produce architectural plans from color codes. Hence one of the first automated architectural plans where generated by simple inputs. As the system develops, these inputs can be points that architects consider before drawing the architectural plans. This will lead to an AI system that needs only the location where the building needs to be constructed and other variables such as budget, rest of the data will automatically be considered by the computer and a suitable design will be generated. Zoning data, building codes, and disabled design data along with other relevant information can be easily stored in the systems memory for the computer to use every time it designs, this data will be readily available for the AI system as well as for the architect (Malaeb, J., & Ma, W, 2019). Liberating architects from the burden of remembering and searching for standards every time they design and allowing them to focus on tweaking the automatically generated plans by the systems. This will lead to a better workflow and in turn a better design. Changes made by architects to the computer generated plans can be studied and noted by the AI system as references and when faced by a similar satiation in the future, the system will have a better and more effective response than the previous time it design. This will lead to a continuous learning process for the system, learning from previous mistakes and evolving into an even powerful machine.
2.3.
Computers, from design tools to design assistants
The creativity of AI lies in its ability to learn and create variation in data and high computing power of systems available these days, thousands of random and usable variations of the input is produced. This has been supported by an experiment where artificial neural networks were trained based on the detailed configuration of the Roman Corinthian order capitals. The successfully trained artificial neural networks was able to generate 3dimensional variations of the new capital forms based on the given input parameters, these variations were both random and purposeful (Cudzik, J., & Radziszewski, K, 2018). 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, it might find ways to learn based on the architect’s inputs. Advancements in AI has changed the idea of how we perceive machines. Once 7|Page
considered a design tool, they now being involved in the decision-making process with the architects. Hence, modifying the role of architects. 3. AI transforming the design process
3.1.
Architectural drawing recognition and generation
Fig 2. Workflow of a typical GAN
A successful research and experiment conducted by Weixin Huang (Tsinghua University) and Hao Zheng (University of Pennsylvania), explored the use of Generative Adversarial Network (GAN) recognizing and generating architectural drawings. GAN is a framework in machine learning, specially designed to learn and generate output data with similar or identical characteristics (Huang, W., & Zheng, H. 2018). This showed that Artificial Intelligence can play a significant role in not only repetitive works, but also creative works. This study also deals with the possibility that human design ability would be greatly expanded when combined with artificial intelligence. The selected case provides a great example by showing how machinic learning can be integrated into architecture design, producing automated architectural plans based on inputs. It showcases how the barrier of technology can be pushed and bring us closer to an automated design process, a step at a time. This study guides me in solving the research question by proving that, the current technology is powerful enough in automating one of the most basic elements of architecture, the design of an architectural plan. The experimenters were able to train a Generative Adversarial Network by assigning color codes to each aspect of an architecture plan, for example the walkway was assigned red color, bedrooms were lite green, windows were dark red, doors dark green and so on. Then they fed several architectural plans to the machine and it was able to produce a colorcoded plan. Then they reversed this process, training the machine to produce architectural plans from color codes. As the system evolved, the generated plans 8|Page
became more vivid. Studies like these, encourage scientists and designers to push the boundaries of technology and architecture, leading the way for further enhancements in this field and others (Huang, W., & Zheng, H. 2018).
Fig 3. Color codes given to each part of the floor plan
Fig 4. Color map generated from floor plan
Fig 5. Floor plan generated from colored map
Hence, it is now possible for Artificial Intelligence systems to generate architectural floor plans using color codes. If technology moves one step ahead and the computer is able to generate these color codes automatically, by minimal human input, then we could automate the floor plan generating process. 3.2.
From plans to architectural element AI generated plans can be extruded into an entire 3D building by systems used in the gaming industries to create entire cities. This system works by creating tree 9|Page
maps which divide and subdivides area into different functions. The tree diagram is responsible for mapping out the functions required in a project, this tree diagram is then converted into a tree map. (RauppMusse, FernandoMarson, & Soraia, 2010)
Fig 6. Tree diagram (a) and related tree map (b)
All the tree maps are gathered along with construction parametric and layout constraints, the information is inserted into a floor plan generator which uses the tree maps as bassline design and generates floor plans. The connections between the rooms and opening are automatically created based on each space, this is achieved by training the system with thousands of floor plans, and the AI system is able to learn from the data provided and utilize it in solving problems. (RauppMusse, FernandoMarson, & Soraia, 2010)
Fig 7. Floor plan generation process
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Now the system uses the 2-dimensional generated plan to create walls and opening. The walls are extruded from the plan and given a height set by the designer opening for doors and windows are created based on algorithms. The system is also able to place basic furniture’s based on the type of function a space adheres. (RauppMusse, FernandoMarson, & Soraia, 2010)
Fig 8. Walls and opening generated from a 2D floor plan
As the system develops, it will be able to accept floor plans generated by other platforms and systems. This will automate several aspects of floor plan generation and 3D modeling using software.
3.3.
Façade Modeling Using Deep Learning As the interior spaces are generated using AI, the exterior spaces are created simultaneously. There have been significant advancements in Facade design using complex systems and software Facade recognition and modeling is possible by the means of image sensors and ground control points. Using georeferenced data and morphological image processing techniques, designers are now able to generate computerized 3D models of facades by an RGB image. Multiple overlapping images of the facade are stitched together to produce a large destruction which minimizes deformities in the model. Georeferenced 3D cloud points are generated by line segments and points from the image, image sensors are able to produce depth in the model. Finally, texture is sourced from the image data and the model is given materiality. (Bacharidis, Ragia, & Lemonia, 2020)
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Fig 9. 3D building model and faรงade reconstruction, by combining image and georeferenced point data.
A more advanced approach in faรงade modeling is catered by the use of GAN (generative adversarial network). It is a branch of machine learning which deals with pixel-to-pixel data (Pix2Pix). GAN can be easily trained to map out data from a 2D image source. The system works by producing a 3D depth point could which is overlapped on the data produced by the Pix2Pix networks. The position of points in the 3D point clouds are refined by georeferenced data. The data is able to iteratively correct point positions in space. A surface reconstruction algorithm and texture mapping technique creates a realistic 3D model of the faรงade. (Bacharidis, Ragia, & Lemonia, 2020)
Fig 10. Updated workflow for generating a 3D building model and utilizing AI based technology and georeferenced data
3.4.
Automated design variations AI is also aiding designers by creating design variations from standard designs. This process works by providing several images and details of an architectural element. The neural network is then trained on that design and the training is 12 | P a g e
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)
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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. 14 | P a g e
The designers can easily go back and make changes to the constraints, Delve 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.
4.1.2. 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
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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)
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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) 4.1.3. 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
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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)
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Fig 20. Points mapped out in 3D space, used to create computer model
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) 4.1.4. 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
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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. 4.1.5. Case Study Comparison Case Studies Implication
4.2.
Scale
Scope
Brief
Delve
General
Medium Scale (City Blocks) Concept design to execution Generative design tool run by AI
AI City
Specific
Medium Scale (City Blocks) Concept design to planning
Recoding Syria
Specific
Large Scale (Entire City)
Concept design
City operations controlled by AI Re-design of buildings by AI
Interview
4.2.1. 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. 4.2.2. 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. 20 | P a g e
He suggests that current AI Technology are not suitable for small firms 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 and will become the norm. 4.3.
Survey
4.3.1. 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. 4.3.2. 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. 21 | P a g e
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. 5. 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. 6. 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 22 | P a g e
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-intelligence23 | P a g e
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 Faรงade 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
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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? Appendix 2 The following graphs represent results obtained from the survey, along with the questions.
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