Artificial Intelligence Applications for High-Performance Built Environments
- 1st Edition - September 1, 2026
- Latest edition
- Authors: Shuai Lu, Dingwen ‘Nic’ Bao, Yi Tan
- Language: English
Artificial Intelligence Applications for High-Performance Built Environments draws on the authors’ international academic and industry experience to provide readers with a compr… Read more
Artificial Intelligence Applications for High-Performance Built Environments draws on the authors’ international academic and industry experience to provide readers with a comprehensive overview of the beneficial impact of AI to the building industry, with both theoretical insights and real-world applications. Demonstrates to readers how many building industry challenges can be successfully addressed using intelligent technologies. Presents detailed, cutting-edge methods of applying AI to a range of building industry procedures and performance aspects of the built environment, enabling readers to use AI solutions to address similar problems they may be confronted with in their projects/investigations. Suggests models, algorithms and tools providing a practical pathway for readers to apply AI technologies across the entire building process lifecycle to enhance innovation, efficiency and productivity. The emphasis on intelligence technologies appeals equally to academic and industry-based readers who are interested in getting in front of the sector’s technology curve and also discovering innovative, practical solutions
- The Application of Intelligent Technologies to High-Performance Built Environments’ is a comprehensive, lifecycle-based resource on the application of intelligent technologies to the entire building process, from early stage planning and generative design, to structural optimisation, fabrication and construction management to long-term operation
- Unlike many existing works that focus narrowly on either design or construction, ‘The Application of Intelligent Technologies to High-Performance Built Environments’ offers a coherent, authored narrative that connects state-of-the-art AI technologies with practical, high-impact challenges in the built environment
- Describes cutting edge technologies including diffusion models, transformer models, computer vision and deep learning, and highlights the remarkable potential for their application to the building industry
Academics, researchers and professional practitioners in the fields of architecture, engineering and construction management, environmental engineering, architecture and design
1. Introduction
1.1 Background: Challenges in the architecture, engineering and construction (AEC) Industry
1.2 Rise of Artificial Intelligence (AI) in Built Environment
1.3 Scope and objectives of this book
1.4 Structure of the book
2. Planning
2.1 Multi-scale planning framework
2.2 Site analysis and functional zoning using AI
2.3 AI-driven architectural programming and space planning
2.4 Early-stage performance prediction and multi-objective trade-offs
2.5 Design brief automation and knowledge transfer
3. Massing
3.1 Principles of massing for high-performance architecture
3.2 Generative models for form exploration
3.3 Performance-driven massing optimization
3.4 AI-augmented design feedback and iterative refinement
3.5 Case studies: AI-assisted architectural form finding
4. Swarm intelligence and generative design
4.1 Swarm intelligence and multi-agent principles in design
4.2 Multi-agent collaboration for spatial configuration
4.3 Generative design pipelines in the AEC industry
4.4 Exploring and optimizing design space with collective intelligence
4.5 Case studies: Swarm and agent-based models in architectural design
5. Structural optimization
5.1 Introduction to structural optimization in architecture
5.2 Topology optimization methods for architectural structures
5.3 Multi-objective optimization strategies
5.4 Integrating structural optimization with robotic fabrication
5.5 Case studies: implementing structural optimization in practice
6. New materials and digital fabrication
6.1 Smart and adaptive materials in architecture
6.2 AI-driven additive manufacturing in architecture
6.3 Data-driven material-form integration
6.4 Learning-enabled robotic fabrication
6.5 Generative design-to-fabrication workflows
6.6 Case studies: AI-integrated digital construction
7. Construction management and digital twin
7.1 Introduction to digital twins in construction
7.2 AI for project scheduling, cost and quality management
7.3 AI for project risk management
7.4 Integration of AI with BIM and IoT
7.5 Real-time monitoring and predictive analytics
7.6 Case studies: digital twin applications in construction
8. Industrial construction
8.1 Evolution of industrialized construction
8.2 AI-enhanced modular design and prefabrication
8.3 Automation and robotics in industrial construction
8.4 Construction supply chain optimization through AI
8.5 Case studies: AI in industrial construction projects
9. Operation and maintenance
9.1 Smart building operations with AI
9.2 Predictive maintenance strategies
9.3 Energy management and sustainability
9.4 Carbon emission reduction with AI
9.5 Enhancing occupant experience through AI
9.6 Case studies: AI in facility management
10. Human-centric understanding
10.1 Human-centered design in the AI era
10.2 Modeling human perception and behavior
10.3 AI for thermal, visual, and acoustic comfort prediction
10.4 Personalized design through adaptive intelligence
10.5 Case Studies: AI-enhanced human-centric building applications
1.1 Background: Challenges in the architecture, engineering and construction (AEC) Industry
1.2 Rise of Artificial Intelligence (AI) in Built Environment
1.3 Scope and objectives of this book
1.4 Structure of the book
2. Planning
2.1 Multi-scale planning framework
2.2 Site analysis and functional zoning using AI
2.3 AI-driven architectural programming and space planning
2.4 Early-stage performance prediction and multi-objective trade-offs
2.5 Design brief automation and knowledge transfer
3. Massing
3.1 Principles of massing for high-performance architecture
3.2 Generative models for form exploration
3.3 Performance-driven massing optimization
3.4 AI-augmented design feedback and iterative refinement
3.5 Case studies: AI-assisted architectural form finding
4. Swarm intelligence and generative design
4.1 Swarm intelligence and multi-agent principles in design
4.2 Multi-agent collaboration for spatial configuration
4.3 Generative design pipelines in the AEC industry
4.4 Exploring and optimizing design space with collective intelligence
4.5 Case studies: Swarm and agent-based models in architectural design
5. Structural optimization
5.1 Introduction to structural optimization in architecture
5.2 Topology optimization methods for architectural structures
5.3 Multi-objective optimization strategies
5.4 Integrating structural optimization with robotic fabrication
5.5 Case studies: implementing structural optimization in practice
6. New materials and digital fabrication
6.1 Smart and adaptive materials in architecture
6.2 AI-driven additive manufacturing in architecture
6.3 Data-driven material-form integration
6.4 Learning-enabled robotic fabrication
6.5 Generative design-to-fabrication workflows
6.6 Case studies: AI-integrated digital construction
7. Construction management and digital twin
7.1 Introduction to digital twins in construction
7.2 AI for project scheduling, cost and quality management
7.3 AI for project risk management
7.4 Integration of AI with BIM and IoT
7.5 Real-time monitoring and predictive analytics
7.6 Case studies: digital twin applications in construction
8. Industrial construction
8.1 Evolution of industrialized construction
8.2 AI-enhanced modular design and prefabrication
8.3 Automation and robotics in industrial construction
8.4 Construction supply chain optimization through AI
8.5 Case studies: AI in industrial construction projects
9. Operation and maintenance
9.1 Smart building operations with AI
9.2 Predictive maintenance strategies
9.3 Energy management and sustainability
9.4 Carbon emission reduction with AI
9.5 Enhancing occupant experience through AI
9.6 Case studies: AI in facility management
10. Human-centric understanding
10.1 Human-centered design in the AI era
10.2 Modeling human perception and behavior
10.3 AI for thermal, visual, and acoustic comfort prediction
10.4 Personalized design through adaptive intelligence
10.5 Case Studies: AI-enhanced human-centric building applications
- Edition: 1
- Latest edition
- Published: September 1, 2026
- Language: English
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Shuai Lu
Dr Shuai Lu is an associate professor at the Institute of Future Human Habitats, Shenzhen International Graduate School, Tsinghua University. Before joining Tsinghua, he was a Lecturer at the University of Sydney. He holds bachelor and Ph.D. degrees from Tsinghua University, and has visiting positions at the National University of Singapore, Technical University of Munich and RWTH Aachen University. Shuai’s research focuses on the interaction of architecture, architectural science and computer science
Affiliations and expertise
Associate Professor, Institute of Future Human Habitats, Shenzhen International Graduate School, Tsinghua University, ChinaDB
Dingwen ‘Nic’ Bao
Dr. Dingwen ‘Nic’ Bao is a Senior Lecturer in Architecture, Architecture Technology Stream Coordinator, and Director of the FormX Research Lab at RMIT University. He currently serves as a Chief Investigator (CI) for an Australian Research Council (ARC) project and a RACE 2023 project. Dr Bao specializes in advanced architectural design, computational design, topology optimization, behavioral algorithms, additive manufacturing, and robotic fabrication
Affiliations and expertise
Senior Lecturer in Architecture, Architecture Technology Stream Coordinator, and Director of the FormX Research Lab, RMIT University, AustraliaYT
Yi Tan
Dr. Yi Tan is an associate professor at the Department of Engineering Management Science, College of Civil and Transportation Engineering, Shenzhen University (SZU). Before joining SZU, he was a research fellow at Nanyang Technological University. He holds a bachelor's degree from Chongqing Jiaotong University, master's degree from University of Southern California, USA and Ph.D. degree from the Hongkong University of Science and Technology. Yi’s research focuses on smart construction, building information modeling, and digital twin
Affiliations and expertise
Associate Professor, Department of Engineering Management Science, College of Civil and Transportation Engineering, Shenzhen University, China