Artificial intelligence in performance-driven design theories, methods, and tools

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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Weitere Verfasser: Abbasabadi, Narjes (HerausgeberIn), Ashayeri, Mehdi (HerausgeberIn)
Format: UnknownFormat
Sprache:eng
Veröffentlicht: Hoboken, New Jersey Wiley 2024
Schlagworte:
Online Zugang:Cover
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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 an
"The recent advances in data-driven approaches and big data initiatives provide enormous opportunities for sustainable design and research in decarbonization and digital transformation of the built environment. The Artificial Intelligence and Sustainable Design: Theories, Methods, and Tools book aims to comprehensively review the application of Artificial Intelligence (AI), particularly Machine Learning (ML), in enhancing performance-based design and offer advanced insight into different approaches, methods, and tools for bringing the power of AI in simulation platforms and leveraging digital twin throughout the life cycle of the built environment. The contents center on fundamentals of building science, including key performance indicators such as comfort, indoor/outdoor environmental quality, solar and daylight, and energy exploring human-centered and evidence-based design and optimization procedures and integrating intelligent systems for better understanding, designing, and managing our existing and future built environments"--
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 AIs 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. Its also of interest to students of architecture, building science and technology, urban design and planning, environmental engineering, and computer science and engineering
Beschreibung:Includes bibliographical references and index
Beschreibung:xix, 283 Seiten
Illustrationen, Diagramme
ISBN:9781394172061
978-1-394-17206-1