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The Relationship Between Artificial Intelligence (AI) and Building Information Modeling (BIM) Technologies for Sustainable Building in the Context of Smart Cities

Author

Listed:
  • Jinyi Li

    (School of Design, South China University of Technology, Guangzhou 510006, China
    These authors contributed equally to this work.)

  • Zhen Liu

    (School of Design, South China University of Technology, Guangzhou 510006, China
    Digital Intelligence Enhanced Design Innovation Laboratory, Key Laboratory of Philosophy and Social Science in General Universities of Guangdong Province, Guangzhou 510006, China
    These authors contributed equally to this work.)

  • Guizhong Han

    (School of Design, South China University of Technology, Guangzhou 510006, China)

  • Peter Demian

    (School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK)

  • Mohamed Osmani

    (School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK)

Abstract

The development of information technologies has been exponentially applied to the architecture, engineering, and construction (AEC) industries. The extent of the literature reveals that the two most pertinent technologies are building information modeling (BIM) and artificial intelligence (AI) technologies. The radical digitization of the AEC industry, enabled by BIM and AI, has contributed to the emergence of “smart cities”, which uses information technology to improve urban operational and sustainable efficiency. Few studies have investigated the roles of AI and BIM in AEC from the perspective of sustainable buildings in assisting designers to make sustainable decisions at building and city levels. Therefore, the purpose of this paper is to explore the research status and future development trends in the relationship between AI and BIM-aided sustainable building in the context of the smart city to provide researchers, designers, and technology developers with potential research directions. This paper adopted a macro and micro bibliographic method, which is used to map out the general research landscape. This is followed by a more in-depth analysis of the fields of sustainable design, sustainable construction, sustainable development, and life cycle assessment (LCA). The results show that the combination of AI and BIM helps to make optimal decisions on materials, cost, energy, construction scheduling, and monitoring and promotes the development of sustainable buildings in both technical and human aspects so to achieve Sustainable Development Goals 7 (ensuring access to affordable, reliable, and sustainable modern energy for all), 9 (building resilient infrastructure, promote inclusive and sustainable industries, and foster innovation), 11 (building inclusive, safe, risk-resilient, and sustainable cities and human settlements), and 12 (ensuring sustainable consumption and production patterns). In addition, the combination of AI, BIM, and LCA technologies offers great potential to improve building performance, and the future development of AI and BIM integration should not only consider the sustainability of buildings but also consider the human-centered design concept and the health, safety, and comfort of stakeholders as one of the goals to realize the multidimensional development of smart city based on city information model.

Suggested Citation

  • Jinyi Li & Zhen Liu & Guizhong Han & Peter Demian & Mohamed Osmani, 2024. "The Relationship Between Artificial Intelligence (AI) and Building Information Modeling (BIM) Technologies for Sustainable Building in the Context of Smart Cities," Sustainability, MDPI, vol. 16(24), pages 1-38, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:24:p:10848-:d:1541484
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    References listed on IDEAS

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    2. Weijun Liu & Yuan Zeng & Dingli Liu & Yao Huang & Yunfei Hou, 2025. "Intelligent Construction-Driven Transformation of Construction Management Education for Sustainable Development: From the Educator’s Perspective," Sustainability, MDPI, vol. 17(20), pages 1-18, October.
    3. Ali Mansouri & Mohsen Naghdi & Abdolmajid Erfani, 2025. "Machine Learning for Leadership in Energy and Environmental Design Credit Targeting: Project Attributes and Climate Analysis Toward Sustainability," Sustainability, MDPI, vol. 17(6), pages 1-19, March.

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