Artificial Intelligence in Performance-Driven Design

Book description


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

  1. Cover
  2. Table of Contents
  3. Title Page
  4. Copyright
  5. List of Contributors
  6. Introduction
    1. References
  7. 1 Augmented Computational Design
    1. Introduction
    2. Background
    3. Framework
    4. Demonstration
    5. Results
    6. Discussion
    7. Outlook
    8. Acronyms
    9. Notations
    10. References
  8. 2 Machine Learning in Urban Building Energy Modeling
    1. Introduction
    2. Urban Building Energy Modeling Methods
    3. Uncertainty in Urban Building Energy Modeling
    4. Machine Learning in Urban Building Energy Modeling
    5. Machine Learning‐Based Surrogate UBEM
    6. Conclusion
    7. References
  9. 3 A Hybrid Physics‐Based Machine Learning Approach for Integrated Energy and Exposure Modeling
    1. Introduction
    2. Materials and Methods
    3. Results
    4. Discussion
    5. Conclusion
    6. Acknowledgment
    7. References
  10. 4 An Integrative Deep Performance Framework for Daylight Prediction in Early Design Ideation
    1. Introduction
    2. Background
    3. Research Methods
    4. Discussions of Results
    5. Conclusions
    6. References
  11. 5 Artificial Intelligence in Building Enclosure Performance Optimization: Frameworks, Methods, and Tools
    1. Building Envelope and Performance
    2. Artificial Intelligence and Building Envelope Overview
    3. Optimization Routes and Building Envelope
    4. Optimization Frameworks
    5. Optimization Methods
    6. Machine Learning and Building Envelope
    7. Artificial Neural Network
    8. Convolutional Neural Network
    9. Recurrent Neural Network
    10. Generative Adversarial Networks
    11. Ensemble Learning
    12. Discussions on Practical Implications
    13. Summary and Conclusion
    14. References
  12. 6 Efficient Parametric Design‐Space Exploration with Reinforcement Learning‐Based Recommenders
    1. Introduction
    2. Methodology
    3. Design Dashboard
    4. Discussion
    5. Conclusion
    6. References
  13. 7 Multi‐Level Optimization of UHP‐FRC Sandwich Panels for Building Façade Systems
    1. Introduction
    2. Building Façade Design Optimization
    3. Methodology
    4. Results and Discussion
    5. Conclusion
    6. References
  14. 8 Decoding Global Indoor Health Perception on Social Media Through NLP and Transformer Deep Learning
    1. Introduction
    2. Literature Review
    3. Materials and Methods
    4. Visualizations
    5. Results and Discussion
    6. Conclusion
    7. References
  15. 9 Occupant‐Driven Urban Building Energy Efficiency via Ambient Intelligence
    1. Introduction
    2. Occupancy and Building Energy Use
    3. Occupant Monitoring Methods
    4. Occupant‐driven Energy Efficiency via Ambient Intelligence
    5. Conclusion
    6. References
  16. 10 Understanding Social Dynamics in Urban Building and Transportation Energy Behavior
    1. Introduction
    2. Methodology
    3. Results and Discussion
    4. Conclusion
    5. References
  17. 11 Building Better Spaces: Using Virtual Reality to Improve Building Performance
    1. Introduction
    2. Applications of Virtual Reality in Building Performance
    3. Conclusion
    4. References
  18. 12 Digital Twin for Citywide Energy Modeling and Management
    1. Introduction
    2. Urban Building Energy Digital Twins (UBEDTs)
    3. Enabling Technologies
    4. Maturity Levels
    5. Architecture
    6. Challenges in Implementing Citywide Digital Twins
    7. Conclusion
    8. References
  19. Index
  20. End User License Agreement

Product information

  • Title: Artificial Intelligence in Performance-Driven Design
  • Author(s): Narjes Abbasabadi, Mehdi Ashayeri
  • Release date: May 2024
  • Publisher(s): Wiley
  • ISBN: 9781394172061