Providing the most comprehensive source available, this book surveys the state of the art in artificial intelligence (AI) as it relates to architecture. This book is organized in four parts: theoretical foundations, tools and techniques, AI in research, and AI in architectural practice. It provides a framework for the issues surrounding AI and offers a variety of perspectives. It contains 24 consistently illustrated contributions examining seminal work on AI from around the world, including the United States, Europe, and Asia. It articulates current theoretical and practical methods, offers critical views on tools and techniques, and suggests future directions for meaningful uses of AI technology. Architects and educators who are concerned with the advent of AI and its ramifications for the design industry will find this book an essential reference.
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viii | |
Preface |
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xvii | |
Acknowledgments |
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xxi | |
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PART 1 Background, history, and theory of Al |
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1 | (90) |
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1 Significant others: machine learning as actor, material, and provocateur in art and design |
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3 | (10) |
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2 Sculpting spaces of possibility: brief history and prospects of artificial intelligence in design |
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13 | (16) |
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3 Mapping generative models for architectural design |
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29 | (30) |
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4 The network of interactions for an artificial architectural intelligence |
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59 | (32) |
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PART 2 Al tools, methods, and techniques |
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91 | (114) |
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5 Machine learning in architecture: an overview of existing tools |
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93 | (17) |
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6 Fundamental aspects of pattern recognition in architectural drawing |
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110 | (20) |
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7 Al as a collaborator in the early stage of the design |
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130 | (30) |
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160 | (22) |
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9 Generating new architectural designs using topological Al |
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182 | (23) |
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PART 3 Al in architectural research |
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205 | (44) |
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10 Artificial intelligence in architectural heritage research: simulating networks of caravanserais through machine learning |
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207 | (17) |
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11 A deep-learning approach to real-time solar radiation prediction |
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224 | (8) |
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12 Artificial intelligence and machine learning in landscape architecture |
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232 | (17) |
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PART 4 Case studies of Al in architecture |
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249 | (204) |
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13 Combining Al and BIM in the design and construction of a Mars habitat |
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251 | (29) |
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14 Toward dynamic and explorative optimization for architectural design |
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280 | (21) |
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15 Synergizing smart building technologies with data analytics |
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301 | (14) |
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16 Explainable ML: augmenting the interpretability of numerical simulation using Bayesian networks |
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315 | (21) |
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17 Image analytics for strategic planning |
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336 | (14) |
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18 Urban development predictor: using development pipeline data to predict San Francisco's growth |
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350 | (14) |
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19 Al in crowdsourced design: sourcing collective design intelligence |
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364 | (16) |
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20 Interfacing architecture and artificial intelligence: machine learning for architectural design and fabrication |
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380 | (14) |
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21 Machining and machine learning: extending architectural digital fabrication through Al |
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394 | (11) |
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22 Augmented intuition: encoding ideas, matter, and why it matters |
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405 | (15) |
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23 Al and architecture: an experimental perspective |
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420 | (22) |
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24 An anonymous composition: a case study of form-finding optimization through a machine learning algorithm |
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442 | (11) |
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Index |
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453 | |
Imdat As is the recipient of the prestigious International Fellowship for Outstanding Researchers and a grant from the Scientific and Technological Research Council of Turkey (TUBITAK) and researches and teaches at the Istanbul Technical University (ITU). Imdat received his BArch from the Middle East Technical University (METU), his MSc in architecture from the Massachusetts Institute of Technology (MIT), and his doctorate from the Harvard University Graduate School of Design. He has coauthored Dynamic Digital Representations in Architecture: Visions in Motion (Taylor & Francis, 2008). In 2011, he founded Arcbazar.com, a first-of-its-kind crowdsourcing platform for architectural design, which has been featured as one of the "Top 100 Most Brilliant Companies" by Entrepreneur magazine. In 2017, he used Arcbazars design data through a DARPA-funded research project to generate conceptual designs via artificial intelligence (AI). Imdat is currently heading the City Design through Design Intelligence (CIDDI) lab at ITU and investigates the impact of emerging technologies on urban morphology and the future of the city.
Prithwish Basu is a principal scientist at Raytheon BBN Technologies (BBN). He has a PhD in computer engineering from Boston University (2003) and a BTech in computer science and engineering from the Indian Institute of Technology (IIT), Delhi (1996). Prithwish has been the Principal Investigator of several U.S. government funded research projects on networking and network science during his 17-year tenure at BBN. He was the Program Director for U.S. Army Research Laboratorys Network Science Collaborative Technology Alliance (NS CTA) program, which ran from 2009 until early 2020, and made fundamental contributions to advancing the state-of-the-art for the science of dynamic intertwined multigenre networks. Prithwish also led the DARPA-funded Fundamental Design (FUN Design) in 20172018, which explored the application of state-of-the-art AI/ML algorithms for graphs encoding architectural design data. Currently, he is leading the development of algorithms in the DARPA-funded FastNICs program for speeding up deep neural network (DNN) training by automatically parallelizing DNN workloads on fast network hardware. Prithwish recently served as an associate editor for the IEEE Transactions of Mobile Computing and was the lead guest editor for the IEEE Journal of Selected Areas in Communications (JSAC) special issue on network science. He has co-authored over 110 peer-reviewed articles (in conferences, journals, and book chapters) and has won the best paper award at IEEE NetSciCom 2014 and PAKDD 2014. He was also a recipient of the MIT Technology Reviews TR35 (Top 35 Innovators Under 35) award in 2006.