The Routledge Companion to Artificial Intelligence in Architecture  book cover
1st Edition

The Routledge Companion to Artificial Intelligence in Architecture

  • Available for pre-order. Item will ship after May 5, 2021
ISBN 9780367424589
May 5, 2021 Forthcoming by Routledge
512 Pages 341 B/W Illustrations

SAVE ~ $75.00
was $250.00
USD $175.00

Prices & shipping based on shipping country


Book Description

Providing the most comprehensive source available, this book surveys the state of the art in Artificial Intelligence (AI) as it relates to architecture. Organised 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 twenty-four 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.

Table of Contents

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



View More



Imdat As is the recipient of the prestigious International Fellowship for Outstanding Researchers and Grant by TUBITAK (The Scientific and Technological Research Council of Turkey) and researches and teaches at Istanbul Technical University. Imdat received his B.Arch. from METU (Middle East Technical University), his M.Sc. in Arch. from MIT (Massachusetts Institute of Technology), and his doctorate from the Harvard University Graduate School of Design. He co-authored, Dynamic Digital Representations in Architecture: Visions in Motion (Taylor & Francis, 2008). In 2011, he founded, 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. Imdat is currently heading the CIDDI lab (City Design through Design Intelligence) 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 Ph.D. in Computer Engineering from Boston University (2003) and a B.Tech. in Computer Science and Engineering from Indian Institute of Technology (IIT) Delhi (1996). Prithwish has been the Principal Investigator (PI) 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 multi-genre 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 best paper awards at IEEE NetSciCom 2014 and PAKDD 2014. He was also a recipient of MIT Technology Review’s TR35 (top 35 innovators under 35) award in 2006.