Artificial Intelligence in Performance-Driven Design: Theories, Methods, and Tools
暫譯: 以績效為驅動的設計中的人工智慧：理論、方法與工具
Abbasabadi, Narjes, Ashayeri, Mehdi
相關主題
商品描述
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.
商品描述(中文翻譯)
性能驅動設計中的人工智慧
使用人工智慧技術和數據驅動方法進行可持續設計的權威跨學科參考
性能驅動設計中的人工智慧：理論、方法與工具 探討了人工智慧（AI），特別是機器學習（ML）在建築環境中的性能建模應用。本書發展了利用AI/ML的理論基礎和方法框架，強調多尺度建模，涵蓋能量流、環境質量和人類系統。
本書檢視了相關實踐、案例研究和計算工具，這些工具利用AI在建模框架中的能力，提升基於物理的模擬、優化和自動化過程的效率、準確性和整合性。此外，它強調在建築環境的整個生命周期中整合智能系統和數位雙胞胎，以增強我們對這些複雜環境的理解和管理。
本書還：
- 將新興技術融入實用理念，以改善性能分析和可持續設計
- 呈現數據驅動的方法論和技術，整合到建模和設計平台中
- 分享有價值的見解和工具，以發展城市建築的脫碳路徑
- 包括來自各相關領域專家研究者和教育者的貢獻
性能驅動設計中的人工智慧 非常適合參與可持續設計和建築環境的建築師、工程師、規劃師和研究人員。對於建築學、建築科學與技術、城市設計與規劃、環境工程以及計算機科學與工程的學生也具有興趣。
作者簡介
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.
作者簡介(中文翻譯)
納爾傑斯·阿巴薩巴迪 (Narjes Abbasabadi) 博士是華盛頓大學建築系的助理教授。阿巴薩巴迪博士同時領導可持續智能實驗室 (Sustainable Intelligence Lab, SIL)。她的研究重點在於建築環境中的可持續性與計算。阿巴薩巴迪的主要關注點是通過開發數據驅動和基於物理的方法、框架和工具，推進設計研究，這些方法利用數位技術，包括人工智慧和機器學習，以增強基於性能和以人為中心的設計。她的研究強調多尺度探索，調查城市建築的能源流、人類系統及其在不同尺度上的環境影響——從建築尺度到社區和城市尺度。阿巴薩巴迪的研究已發表於多個領先期刊，包括《應用能源》(Applied Energy)、《建築與環境》(Building and Environment)、《能源與建築》(Energy and Buildings)、《環境研究》(Environmental Research)以及《可持續城市與社會》(Sustainable Cities and Society)。阿巴薩巴迪在伊利諾伊理工學院獲得建築學博士學位，專攻建築環境技術，並持有德黑蘭自由大學的建築碩士和學士學位。
梅赫迪·阿沙耶里 (Mehdi Ashayeri) 博士是南伊利諾伊大學建築學院的助理教授，並領導城市智能與完整性實驗室 (Urban Intelligence and Integrity Lab, URBiiLAB)。阿沙耶里在伊利諾伊理工學院獲得建築學博士學位，專攻建築環境技術。他還擁有德黑蘭自由大學的建築工程碩士學位和土木工程學士學位。阿沙耶里博士的研究集中在環境性能和計算，並強調其對人類健康和正義的影響。這涉及使用數據驅動技術，包括人工智慧、機器學習、自然語言處理、大數據和感測技術，開發框架、工具和數位平台，以及基於物理的模擬方法。在最近的項目中，阿沙耶里特別探索了時空建模、能源性能評估、空氣污染暴露評估以及在不同尺度上整合人類反饋系統。這些研究旨在促進以數據為依據的決策制定，以支持以人為中心的設計，並為可持續建築和城市的發展做出貢獻。阿沙耶里的研究已發表於高影響力的期刊，包括《環境研究》(Environmental Research)、《能源與建築》(Energy and Buildings)、《應用能源》(Applied Energy)、《建築與環境》(Building and Environment)以及《可持續城市與社會》(Sustainable Cities and Society)。
