Digital twin for healthy indoor environment: A vision for the post-pandemic era

Jiannan Cai, Jianli Chen, Yuqing Hu, Shuai Li, Qiang He

Research output: Contribution to journalReview articlepeer-review

11 Scopus citations

Abstract

Indoor environment has significant impacts on human health as people spend 90% of their time indoors. The COVID-19 pandemic and the increased public health awareness have further elevated the urgency for cultivating and maintaining a healthy indoor environment. The advancement in emerging digital twin technologies including building information modeling (BIM), Internet of Things (IoT), data analytics, and smart control have led to new opportunities for building design and operation. Despite the numerous studies on developing methods for creating digital twins and enabling new functionalities and services in smart building management, very few have focused on the health of indoor environment. There is a critical need for understanding and envisaging how digital twin paradigms can be geared towards healthy indoor environment. Therefore, this study reviews the techniques for developing digital twins and discusses how the techniques can be customized to contribute to public health. Specifically, the current applications of BIM, IoT sensing, data analytics, and smart building control technologies for building digital twins are reviewed, and the knowledge gaps and limitations are discussed to guide future research for improving environmental and occupant health. Moreover, this paper elaborates a vision for future research on integrated digital twins for a healthy indoor environment with special considerations of the above four emerging techniques and issues. This review contributes to the body of knowledge by advocating for the consideration of health in digital twin modeling and smart building services and presenting the research roadmap for digital twin-enabled healthy indoor environment.

Original languageEnglish (US)
Pages (from-to)300-318
Number of pages19
JournalFrontiers of Engineering Management
Volume10
Issue number2
DOIs
StatePublished - Jun 2023

All Science Journal Classification (ASJC) codes

  • Decision Sciences (miscellaneous)
  • Management Science and Operations Research

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