LESSONS FROM BIM AND AI FOR INDOOR ENVIRONMENTAL QUALITY MANAGEMENT: APPLICATIONS TO UNDERGROUND SPACE ENVIRONMENTAL QUALITY RESEARCH

19th WORLD CONFERENCE OF THE ASSOCIATED RESEARCH CENTRES FOR THE URBAN UNDERGROUND SPACE, Belgrade, Serbia, November 4-7, 2025. (Paper No: 5.2.115,  pp. 796-807)

 

АУТОР(И) / AUTHOR(S): Samuel Twum-Ampofo, Isabelle Chan, Hao Chen

 

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DOI:  10.46793/ACUUS2025.5.2.115

САЖЕТАК / ABSTRACT:

Academic literature has well established the impact of indoor environmental quality on human wellbeing (health, comfort and productivity). In the case of underground spaces, increasing evidence suggests that the wellbeing of underground occupants may be compromised due to unique indoor environmental constraints. While Building Information Modelling (BIM) and artificial intelligence (AI) hold promise in addressing such risks, the potential these technologies to redefine the affordances of indoor environmental quality in underground spaces remains unknown. Through a bibliometric analysis of scholarly literature, a mapping of the evolution of BIM and AI technology usage in enhancing indoor environmental comfort is reported. The study identified seven thematic clusters of the use of BIM-AI in general IEQ research, with a surge in publications since 2019. However, a significant gap remains in applying BIM-AI technologies for underground space research. Among the technologies reviewed, supervised machine learning emerged as the predominant approach. Insight from the bibliographic themes suggests transformative opportunities to engineer underground spaces into adaptive environments that actively enhance human wellbeing. These include BIM-visualized early warning systems for occupant comfort management and AI-driven automated HVAC control. The results serve as a foundation for designers and policymakers to leverage BIM’s data integration capabilities and AI’s predictive insights to achieve both health-centric design and operation of underground spaces.

КЉУЧНЕ РЕЧИ / KEYWORDS:

Wellbeing, Indoor environmental quality, BIM, AI, Underground Space

ПРОЈЕКАТ / ACKNOWLEDGEMENT:

This work was supported by the General Research Fund (Grant No.17203920) under the Research Grant Council, HKSAR.

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