The Routledge Companion to Artificial Intelligence in Architecture

ISBN-13: 9780367749590 / Miękka / 2023 / 464 str.

Opis

Providing the most comprehensive source available, this book surveys the state of the art in AI as it relates to architecture. 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.

Spis treści

Part 1: Background, History and Theory of AI  1. Significant Others: Machine Learning as Actor, Material, and Provocateur in Art and Design Kyle Steinfeld  2. Sculpting Probabilistic Spaces: Brief History and Prospects of Machine Learning in Design Daniel Cardoso Llach  3. Mapping generative models for architectural design Pedro Veloso, Ramesh Krishnamurti  4. The Network of Interactions for an Artificial Architectural Intelligence Can Uzun  Part 2: AI Tools, Methods and Techniques  5. Machine Learning in Architecture: An Overview of Existing Tool Ilija Vukorep, Anatolii Kotov  6. Fundamental Aspects of Pattern Recognition in Architectural Drawing Tyler Kvochick  7. AI as a collaborator in the early stage of design Sam Conrad Joyce  8. AI in space planning Danil Nagy  9. Generating new architectural designs using topological AI Prithwish Basu, Imdat As, Elizabeth Munch  Part 3 – AI in Architectural Research  10. Artificial Intelligence in Architectural Heritage Research: Simulating Networks of Caravanserais through Machine Learning Guzden Varinlioglu, Özgün Balaban  11. A Deep Learning approach to Real Time Solar Radiation Prediction Theodoros Galanos, Angelos Chronis  12. Artificial Intelligence and Machine Learning in Landscape Architecture Bradley Cantrell, Zihao Zhang, Xun Liu  Part 4 – Case Studies of AI in Architecture  13. Combining AI and BIM in the design and construction of a Mars habitat Naveen K. Muthumanickam, Jose Pinto Duarte, Shadi Nazarian, Ali Memari, Sven G. Bilén  14. Towards Dynamic and Explorative Optimization for Architectural Design David Newton  15. Synergizing smart building technologies with data analytics Andrzej Zarzycki  16. Explainable ML: Augmenting the interpretability of numerical simulation using Bayesian networks Zack Xuereb Conti, Sawako Kaijima  17. Image Analytics for Strategic Planning Aldo Sollazzo  18. Urban Development Predictor SOM (Skidmore, Owings and Merrill)  19. AI in Crowdsourced Design: Sourcing Collective Design Intelligence Imdat As, Prithwish Basu, Sergey Burukin  20. Interfacing Architecture and Artificial Intelligence - Machine Learning for Architectural Design and Fabrication Bastian Wibranek, Oliver Tessmann  21. Machining and machine learning: Extending architectural fabrication through AI Paul Nicholas  22. Augmented Intuition - Encoding Ideas, Matter and why it matters Mathias Bernhard, Maria Smigielska, Benjamin Dillenburger  23. AI & Architecture – An Experimental Perspective Stanislas Chaillou  24. An Anonymous Composition: A Case Study of Form Finding Optimization through Machine Learning Algorithm Akshay Srivastava, Longtai Liao, Henan Liu  25. Turbulent Intelligences: Liquid Architectures in Latent Spaces Marcos Novak  Index

　

O autorze

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 Arcbazar’s 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 Laboratory’s 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 2017–2018, 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 Review’s TR35 (Top 35 Innovators Under 35) award in 2006.