{"product_id":"artificial-intelligence-in-performance-driven-design-isbn-9781394172061","title":"Artificial Intelligence in Performance-Driven Design","description":"\u003cp\u003e \u003cb\u003eA definitive, interdisciplinary reference to using artificial intelligence technology and data-driven methodologies for sustainable design\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eArtificial Intelligence in Performance-Driven Design: Theories, Methods, and Tools \u003c\/i\u003eexplores 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. \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003eThis book also:  \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eIncorporates emerging technologies into practical ideas to improve performance analysis and sustainable design \u003c\/li\u003e\n\u003cli\u003ePresents data-driven methodologies and technologies that integrate into modeling and design platforms \u003c\/li\u003e\n\u003cli\u003eShares valuable insights and tools for developing decarbonization pathways in urban buildings \u003c\/li\u003e\n\u003cli\u003eIncludes contributions from expert researchers and educators across a range of related fields\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eArtificial Intelligence in Performance-Driven Design \u003c\/i\u003eis 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. \u003c\/p\u003e\u003cp\u003eList of Contributors xi\u003c\/p\u003e \u003cp\u003eIntroduction xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Augmented Computational Design 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 1\u003c\/p\u003e \u003cp\u003eBackground 2\u003c\/p\u003e \u003cp\u003eRelevance of AI in AEC 2\u003c\/p\u003e \u003cp\u003eHistorical Context 3\u003c\/p\u003e \u003cp\u003eDesign as Decision-Making 5\u003c\/p\u003e \u003cp\u003eAI for Generative Design 7\u003c\/p\u003e \u003cp\u003eFramework 9\u003c\/p\u003e \u003cp\u003eDesign Space Exploration 11\u003c\/p\u003e \u003cp\u003eSpatial Design Variables 13\u003c\/p\u003e \u003cp\u003eStatistical Approaches to Design 14\u003c\/p\u003e \u003cp\u003eDemonstration 15\u003c\/p\u003e \u003cp\u003eCase Study 15\u003c\/p\u003e \u003cp\u003eMethodology 16\u003c\/p\u003e \u003cp\u003eResults 21\u003c\/p\u003e \u003cp\u003eBBN Validation Results 21\u003c\/p\u003e \u003cp\u003eToy Problem 22\u003c\/p\u003e \u003cp\u003eDiscussion 22\u003c\/p\u003e \u003cp\u003eOutlook 25\u003c\/p\u003e \u003cp\u003eAcronyms 26\u003c\/p\u003e \u003cp\u003eNotations 27\u003c\/p\u003e \u003cp\u003eReferences 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Machine Learning in Urban Building Energy Modeling 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 31\u003c\/p\u003e \u003cp\u003eUrban Building Energy Modeling Methods 32\u003c\/p\u003e \u003cp\u003eTop–Down Models 33\u003c\/p\u003e \u003cp\u003eBottom–Up Models 33\u003c\/p\u003e \u003cp\u003eUncertainty in Urban Building Energy Modeling 36\u003c\/p\u003e \u003cp\u003eEpistemic Uncertainty 36\u003c\/p\u003e \u003cp\u003eStochastic Uncertainty 36\u003c\/p\u003e \u003cp\u003eAddressing Uncertainty 37\u003c\/p\u003e \u003cp\u003eMachine Learning in Urban Building Energy Modeling 39\u003c\/p\u003e \u003cp\u003eSupervised Learning 39\u003c\/p\u003e \u003cp\u003eUnsupervised Learning 44\u003c\/p\u003e \u003cp\u003eReinforcement Learning 46\u003c\/p\u003e \u003cp\u003eMachine Learning-Based Surrogate UBEM 47\u003c\/p\u003e \u003cp\u003eConclusion 49\u003c\/p\u003e \u003cp\u003eReferences 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 A Hybrid Physics-Based Machine Learning Approach for Integrated Energy and Exposure Modeling 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 57\u003c\/p\u003e \u003cp\u003eMaterials and Methods 59\u003c\/p\u003e \u003cp\u003eData, Data Sources, and Dataset Processing 59\u003c\/p\u003e \u003cp\u003eMethodology 61\u003c\/p\u003e \u003cp\u003eResults 70\u003c\/p\u003e \u003cp\u003ePhysics-Based Simulation 70\u003c\/p\u003e \u003cp\u003eData-Driven Computation (Prediction) 70\u003c\/p\u003e \u003cp\u003eDiscussion 73\u003c\/p\u003e \u003cp\u003eConclusion 74\u003c\/p\u003e \u003cp\u003eAcknowledgment 75\u003c\/p\u003e \u003cp\u003eReferences 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 An Integrative Deep Performance Framework for Daylight Prediction in Early Design\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIdeation 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 81\u003c\/p\u003e \u003cp\u003eBackground 83\u003c\/p\u003e \u003cp\u003eDaylight Simulation 84\u003c\/p\u003e \u003cp\u003eDeep Learning Models 85\u003c\/p\u003e \u003cp\u003eDL-Based Surrogate Modeling 85\u003c\/p\u003e \u003cp\u003eVerification Methods 85\u003c\/p\u003e \u003cp\u003eResearch Methods 86\u003c\/p\u003e \u003cp\u003eData Acquisition 86\u003c\/p\u003e \u003cp\u003eModel Training 88\u003c\/p\u003e \u003cp\u003eResults and Validation 88\u003c\/p\u003e \u003cp\u003eDiscussions of Results 90\u003c\/p\u003e \u003cp\u003eConclusions 94\u003c\/p\u003e \u003cp\u003eReferences 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Artificial Intelligence in Building Enclosure Performance Optimization: Frameworks, Methods, and Tools 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBuilding Envelope and Performance 97\u003c\/p\u003e \u003cp\u003eArtificial Intelligence and Building Envelope Overview 97\u003c\/p\u003e \u003cp\u003eOptimization Routes and Building Envelope 98\u003c\/p\u003e \u003cp\u003eOptimization Frameworks 99\u003c\/p\u003e \u003cp\u003eOptimization Methods 99\u003c\/p\u003e \u003cp\u003eMachine Learning and Building Envelope 101\u003c\/p\u003e \u003cp\u003eArtificial Neural Network 101\u003c\/p\u003e \u003cp\u003eConvolutional Neural Network 105\u003c\/p\u003e \u003cp\u003eRecurrent Neural Network 105\u003c\/p\u003e \u003cp\u003eGenerative Adversarial Networks 106\u003c\/p\u003e \u003cp\u003eEnsemble Learning 107\u003c\/p\u003e \u003cp\u003eDiscussions on Practical Implications 108\u003c\/p\u003e \u003cp\u003eSummary and Conclusion 109\u003c\/p\u003e \u003cp\u003eReferences 110\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Efficient Parametric Design-Space Exploration with Reinforcement Learning-Based Recommenders 113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 113\u003c\/p\u003e \u003cp\u003eMethodology 115\u003c\/p\u003e \u003cp\u003eSection 01: Clustering Design Options 116\u003c\/p\u003e \u003cp\u003eSection 02: Reinforcement Learning-Based Recommender System 120\u003c\/p\u003e \u003cp\u003eDesign Dashboard 123\u003c\/p\u003e \u003cp\u003eDiscussion 124\u003c\/p\u003e \u003cp\u003eConclusion 125\u003c\/p\u003e \u003cp\u003eReferences 126\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Multi-Level Optimization of UHP-FRC Sandwich Panels for Building Façade Systems 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 129\u003c\/p\u003e \u003cp\u003eBuilding Façade Design Optimization 130\u003c\/p\u003e \u003cp\u003eMethodology 134\u003c\/p\u003e \u003cp\u003eMidspan Displacements and Thermal Resistivity of UHP-FRC Panels 136\u003c\/p\u003e \u003cp\u003eEnergy Performance of the UHP-FRC Panels at the Building Level 141\u003c\/p\u003e \u003cp\u003eLife Cycle Cost Analysis of the UHP-FRC Panels 142\u003c\/p\u003e \u003cp\u003eSurrogate Models 145\u003c\/p\u003e \u003cp\u003eMulti-objective Optimization Algorithm 147\u003c\/p\u003e \u003cp\u003eResults and Discussion 148\u003c\/p\u003e \u003cp\u003eSurrogate Models 148\u003c\/p\u003e \u003cp\u003ePareto Front Solutions 151\u003c\/p\u003e \u003cp\u003eConclusion 152\u003c\/p\u003e \u003cp\u003eReferences 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Decoding Global Indoor Health Perception on Social Media Through NLP and Transformer Deep Learning 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 159\u003c\/p\u003e \u003cp\u003eLiterature Review 161\u003c\/p\u003e \u003cp\u003eSocial Media and Urban Life: Theories, Challenges, and Opportunities 161\u003c\/p\u003e \u003cp\u003eMethods for Computing Social Media Data in Environmental Studies 163\u003c\/p\u003e \u003cp\u003eMaterials and Methods 168\u003c\/p\u003e \u003cp\u003eData Query 168\u003c\/p\u003e \u003cp\u003eText Preprocessing 169\u003c\/p\u003e \u003cp\u003eText Tokenization 169\u003c\/p\u003e \u003cp\u003eText Summarization 170\u003c\/p\u003e \u003cp\u003eGenerating Co-occurrence Matrix 170\u003c\/p\u003e \u003cp\u003eSentiment Analysis and Classification 170\u003c\/p\u003e \u003cp\u003eVisualizations 171\u003c\/p\u003e \u003cp\u003eEmbedding Visualization 171\u003c\/p\u003e \u003cp\u003eAttention Score Visualization (Attention Map) and Interpretation 172\u003c\/p\u003e \u003cp\u003eResults and Discussion 173\u003c\/p\u003e \u003cp\u003eConclusion 178\u003c\/p\u003e \u003cp\u003eReferences 179\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Occupant-Driven Urban Building Energy Efficiency via Ambient Intelligence 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 187\u003c\/p\u003e \u003cp\u003eOccupancy and Building Energy Use 191\u003c\/p\u003e \u003cp\u003eDefinitions 191\u003c\/p\u003e \u003cp\u003eOccupant Monitoring Methods 193\u003c\/p\u003e \u003cp\u003eOccupant Monitoring Via Observational Studies 194\u003c\/p\u003e \u003cp\u003eOccupant Monitoring via Experimental Studies 195\u003c\/p\u003e \u003cp\u003eOccupant-driven Energy Efficiency via Ambient Intelligence 196\u003c\/p\u003e \u003cp\u003eAmbient Intelligence Advancements and Applications 196\u003c\/p\u003e \u003cp\u003eAmI-Based Energy Efficiency Feedback (EEF) Systems 197\u003c\/p\u003e \u003cp\u003eEnergy Efficiency via AmI Systems and Digital Twins Technology 201\u003c\/p\u003e \u003cp\u003eConclusion 202\u003c\/p\u003e \u003cp\u003eReferences 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Understanding Social Dynamics in Urban Building and Transportation Energy Behavior 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 211\u003c\/p\u003e \u003cp\u003eMethodology 213\u003c\/p\u003e \u003cp\u003eModeling Framework 214\u003c\/p\u003e \u003cp\u003eExplanatory Model 214\u003c\/p\u003e \u003cp\u003eData 215\u003c\/p\u003e \u003cp\u003eResults and Discussion 219\u003c\/p\u003e \u003cp\u003eEffects of Occupancy and Socio-economic Factors 219\u003c\/p\u003e \u003cp\u003eVariable Importance (VI) 219\u003c\/p\u003e \u003cp\u003eLek’s Profile 219\u003c\/p\u003e \u003cp\u003eConclusion 226\u003c\/p\u003e \u003cp\u003eReferences 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Building Better Spaces: Using Virtual Reality to Improve Building Performance 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 231\u003c\/p\u003e \u003cp\u003eApplications of Virtual Reality in Building Performance 233\u003c\/p\u003e \u003cp\u003eVirtual Reality for Improving Building Design through Integrated Performance Data 233\u003c\/p\u003e \u003cp\u003eVirtual Reality for Building Design Reviews and Education in Architecture and Engineering 236\u003c\/p\u003e \u003cp\u003eVirtual Reality for Research on Building Occupant Comfort and Well-Being 240\u003c\/p\u003e \u003cp\u003eConclusion 243\u003c\/p\u003e \u003cp\u003eReferences 245\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Digital Twin for Citywide Energy Modeling and Management 251\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 251\u003c\/p\u003e \u003cp\u003eUrban Building Energy Digital Twins (UBEDTs) 252\u003c\/p\u003e \u003cp\u003eDefinition and Conceptualization 252\u003c\/p\u003e \u003cp\u003eImplications for Citywide Energy Management 254\u003c\/p\u003e \u003cp\u003eEnabling Technologies 256\u003c\/p\u003e \u003cp\u003eTwining Technologies 256\u003c\/p\u003e \u003cp\u003eUrban Digital Twin(UDT) and Data Sources 258\u003c\/p\u003e \u003cp\u003eArtificial Intelligence (AI) and Digital Twin 260\u003c\/p\u003e \u003cp\u003eRelationship Between IoT, Big Data, AI–ML, and Digital Twins 261\u003c\/p\u003e \u003cp\u003eInteroperability Technologies 262\u003c\/p\u003e \u003cp\u003eMaturity Levels 263\u003c\/p\u003e \u003cp\u003eArchitecture 265\u003c\/p\u003e \u003cp\u003eData Acquisition Layer 266\u003c\/p\u003e \u003cp\u003eTransmission Layer 266\u003c\/p\u003e \u003cp\u003eModeling and Simulation Layer 266\u003c\/p\u003e \u003cp\u003eData\/Model Integration Layer 269\u003c\/p\u003e \u003cp\u003eService\/Actuation Layer 269\u003c\/p\u003e \u003cp\u003eChallenges in Implementing Citywide Digital Twins 269\u003c\/p\u003e \u003cp\u003eData Quality and Availability 270\u003c\/p\u003e \u003cp\u003eRequired Smart Infrastructure and Associated Cost 270\u003c\/p\u003e \u003cp\u003eInteroperability 270\u003c\/p\u003e \u003cp\u003eData Analysis 271\u003c\/p\u003e \u003cp\u003eCybersecurity and Privacy Concerns 271\u003c\/p\u003e \u003cp\u003eConclusion 272\u003c\/p\u003e \u003cp\u003eReferences 272\u003c\/p\u003e \u003cp\u003eIndex 277\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eNarjes Abbasabadi, PhD, \u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eMehdi Ashayeri, PhD, \u003c\/b\u003e 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.   \u003c\/p\u003e\u003cp\u003e \u003cb\u003eA definitive, interdisciplinary reference to using artificial intelligence technology and data-driven methodologies for sustainable design\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eArtificial Intelligence in Performance-Driven Design: Theories, Methods, and Tools \u003c\/i\u003eexplores 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. \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003eThis book also:  \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eIncorporates emerging technologies into practical ideas to improve performance analysis and sustainable design \u003c\/li\u003e\n\u003cli\u003ePresents data-driven methodologies and technologies that integrate into modeling and design platforms \u003c\/li\u003e\n\u003cli\u003eShares valuable insights and tools for developing decarbonization pathways in urban buildings \u003c\/li\u003e\n\u003cli\u003eIncludes contributions from expert researchers and educators across a range of related fields\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eArtificial Intelligence in Performance-Driven Design \u003c\/i\u003eis 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988765720805,"sku":"NP9781394172061","price":99.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394172061.jpg?v=1761781507","url":"https:\/\/k12savings.com\/es\/products\/artificial-intelligence-in-performance-driven-design-isbn-9781394172061","provider":"K12savings","version":"1.0","type":"link"}