Artificial Intelligence in Performance-Driven Design: Theories, Methods, and Tools

A definitive, interdisciplinary reference to using artificial intelligence technology and data-driven methodologies for sustainable design Artificial Intelligence in Performance-Driven Design: Theories, Methods, and Tools explores the application of artificial intelligence (AI), specifically machine learning (ML), for performance modeling within the built environment. This work develops the theoretical foundations and methodological frameworks for utilizing AI/ML, with an emphasis on multi-scale modeling encompassing energy flows, environmental quality, and human systems. The book examines relevant practices, case studies, and computational tools that harness AI’s capabilities in modeling frameworks, enhancing the efficiency, accuracy, and integration of physics-based simulation, optimization, and automation processes. Furthermore, it highlights the integration of intelligent systems and digital twins throughout the lifecycle of the built environment, to enhance our understanding and management of these complex environments. This book also: Incorporates emerging technologies into practical ideas to improve performance analysis and sustainable design Presents data-driven methodologies and technologies that integrate into modeling and design platforms Shares valuable insights and tools for developing decarbonization pathways in urban buildings Includes contributions from expert researchers and educators across a range of related fields Artificial Intelligence in Performance-Driven Design is ideal for architects, engineers, planners, and researchers involved in sustainable design and the built environment. It’s also of interest to students of architecture, building science and technology, urban design and planning, environmental engineering, and computer science and engineering.

Table of Contents:
List of Contributors xi Introduction xiii 1 Augmented Computational Design 1 Introduction 1 Background 2 Relevance of AI in AEC 2 Historical Context 3 Design as Decision-Making 5 AI for Generative Design 7 Framework 9 Design Space Exploration 11 Spatial Design Variables 13 Statistical Approaches to Design 14 Demonstration 15 Case Study 15 Methodology 16 Results 21 BBN Validation Results 21 Toy Problem 22 Discussion 22 Outlook 25 Acronyms 26 Notations 27 References 28 2 Machine Learning in Urban Building Energy Modeling 31 Introduction 31 Urban Building Energy Modeling Methods 32 Top–Down Models 33 Bottom–Up Models 33 Uncertainty in Urban Building Energy Modeling 36 Epistemic Uncertainty 36 Stochastic Uncertainty 36 Addressing Uncertainty 37 Machine Learning in Urban Building Energy Modeling 39 Supervised Learning 39 Unsupervised Learning 44 Reinforcement Learning 46 Machine Learning-Based Surrogate UBEM 47 Conclusion 49 References 50 3 A Hybrid Physics-Based Machine Learning Approach for Integrated Energy and Exposure Modeling 57 Introduction 57 Materials and Methods 59 Data, Data Sources, and Dataset Processing 59 Methodology 61 Results 70 Physics-Based Simulation 70 Data-Driven Computation (Prediction) 70 Discussion 73 Conclusion 74 Acknowledgment 75 References 75 4 An Integrative Deep Performance Framework for Daylight Prediction in Early Design Ideation 81 Introduction 81 Background 83 Daylight Simulation 84 Deep Learning Models 85 DL-Based Surrogate Modeling 85 Verification Methods 85 Research Methods 86 Data Acquisition 86 Model Training 88 Results and Validation 88 Discussions of Results 90 Conclusions 94 References 94 5 Artificial Intelligence in Building Enclosure Performance Optimization: Frameworks, Methods, and Tools 97 Building Envelope and Performance 97 Artificial Intelligence and Building Envelope Overview 97 Optimization Routes and Building Envelope 98 Optimization Frameworks 99 Optimization Methods 99 Machine Learning and Building Envelope 101 Artificial Neural Network 101 Convolutional Neural Network 105 Recurrent Neural Network 105 Generative Adversarial Networks 106 Ensemble Learning 107 Discussions on Practical Implications 108 Summary and Conclusion 109 References 110 6 Efficient Parametric Design-Space Exploration with Reinforcement Learning-Based Recommenders 113 Introduction 113 Methodology 115 Section 01: Clustering Design Options 116 Section 02: Reinforcement Learning-Based Recommender System 120 Design Dashboard 123 Discussion 124 Conclusion 125 References 126 7 Multi-Level Optimization of UHP-FRC Sandwich Panels for Building Façade Systems 129 Introduction 129 Building Façade Design Optimization 130 Methodology 134 Midspan Displacements and Thermal Resistivity of UHP-FRC Panels 136 Energy Performance of the UHP-FRC Panels at the Building Level 141 Life Cycle Cost Analysis of the UHP-FRC Panels 142 Surrogate Models 145 Multi-objective Optimization Algorithm 147 Results and Discussion 148 Surrogate Models 148 Pareto Front Solutions 151 Conclusion 152 References 153 8 Decoding Global Indoor Health Perception on Social Media Through NLP and Transformer Deep Learning 159 Introduction 159 Literature Review 161 Social Media and Urban Life: Theories, Challenges, and Opportunities 161 Methods for Computing Social Media Data in Environmental Studies 163 Materials and Methods 168 Data Query 168 Text Preprocessing 169 Text Tokenization 169 Text Summarization 170 Generating Co-occurrence Matrix 170 Sentiment Analysis and Classification 170 Visualizations 171 Embedding Visualization 171 Attention Score Visualization (Attention Map) and Interpretation 172 Results and Discussion 173 Conclusion 178 References 179 9 Occupant-Driven Urban Building Energy Efficiency via Ambient Intelligence 187 Introduction 187 Occupancy and Building Energy Use 191 Definitions 191 Occupant Monitoring Methods 193 Occupant Monitoring Via Observational Studies 194 Occupant Monitoring via Experimental Studies 195 Occupant-driven Energy Efficiency via Ambient Intelligence 196 Ambient Intelligence Advancements and Applications 196 AmI-Based Energy Efficiency Feedback (EEF) Systems 197 Energy Efficiency via AmI Systems and Digital Twins Technology 201 Conclusion 202 References 203 10 Understanding Social Dynamics in Urban Building and Transportation Energy Behavior 211 Introduction 211 Methodology 213 Modeling Framework 214 Explanatory Model 214 Data 215 Results and Discussion 219 Effects of Occupancy and Socio-economic Factors 219 Variable Importance (VI) 219 Lek’s Profile 219 Conclusion 226 References 227 11 Building Better Spaces: Using Virtual Reality to Improve Building Performance 231 Introduction 231 Applications of Virtual Reality in Building Performance 233 Virtual Reality for Improving Building Design through Integrated Performance Data 233 Virtual Reality for Building Design Reviews and Education in Architecture and Engineering 236 Virtual Reality for Research on Building Occupant Comfort and Well-Being 240 Conclusion 243 References 245 12 Digital Twin for Citywide Energy Modeling and Management 251 Introduction 251 Urban Building Energy Digital Twins (UBEDTs) 252 Definition and Conceptualization 252 Implications for Citywide Energy Management 254 Enabling Technologies 256 Twining Technologies 256 Urban Digital Twin(UDT) and Data Sources 258 Artificial Intelligence (AI) and Digital Twin 260 Relationship Between IoT, Big Data, AI–ML, and Digital Twins 261 Interoperability Technologies 262 Maturity Levels 263 Architecture 265 Data Acquisition Layer 266 Transmission Layer 266 Modeling and Simulation Layer 266 Data/Model Integration Layer 269 Service/Actuation Layer 269 Challenges in Implementing Citywide Digital Twins 269 Data Quality and Availability 270 Required Smart Infrastructure and Associated Cost 270 Interoperability 270 Data Analysis 271 Cybersecurity and Privacy Concerns 271 Conclusion 272 References 272 Index 277

About the Author :
Narjes Abbasabadi, PhD, is an Assistant Professor in the Department of Architecture at the University of Washington. Dr. Abbasabadi also leads the Sustainable Intelligence Lab (SIL). Her research centers on sustainability and computation within the built environment. Abbasabadi’s primary focus is advancing design research through the development of data-driven and physics-based methods, frameworks, and tools that leverage digital technologies, including artificial intelligence and machine learning, to enhance performance-based and human-centered design. With an emphasis on multi-scale exploration, her research investigates urban building energy flows, human systems, and environmental impacts across scales—from the scale of building to the scale of neighborhood and city. Abbasabadi’s research has been published in leading journals, including Applied Energy, Building and Environment, Energy and Buildings, Environmental Research, and Sustainable Cities and Society. Abbasabadi earned a Ph.D. in Architecture with a specialization in Technologies of the Built Environment, from the Illinois Institute of Technology, and holds Master’s and Bachelor’s degrees in Architecture from Tehran Azad University. Mehdi Ashayeri, PhD, is an Assistant Professor in the School of Architecture at Southern Illinois University, where he leads the Urban Intelligence and Integrity Lab (URBiiLAB). Ashayeri earned his Ph.D. in Architecture–Technologies of the Built Environment, from the Illinois Institute of Technology. He also holds an M.Sc. in Architectural Engineering and a B.Sc. in Civil Engineering from Tehran Azad University. Dr. Ashayeri’s research is centered on environmental performance and computing, with a strong emphasis on their implications for human health and justice. This involves developing frameworks, tools, and digital platforms using data-driven techniques including artificial intelligence, machine learning, natural language processing, Big data, and sensing, as well as physics-based simulation methodologies. In recent projects, Ashayeri has specifically explored spatiotemporal modeling, energy performance evaluation, assessment of exposure to air pollution, and the integration of human feedback systems across various scales. These studies are designed to facilitate data-informed decision-making for human-centered design, as well as to contribute to the development of sustainable buildings and cities. Ashayeri’s research has been published in high-impact journals, including Environmental Research, Energy and Buildings, Applied Energy, Building and Environment, and Sustainable Cities and Society.