ARTIFICIAL INTELLIGENCE IN PERFORMANCE DRIVEN DESIGN THEORIES METHODS AND TOOLS (PB 2024). Dieser Artikel ist nicht verfügbar.ARTIFICIAL INTELLIGENCE IN PERFORMANCE DRIVEN DESIGN THEORIES METHODS AND TOOLS (PB 2024)

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 establishes 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.

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.