A review of the Digital Twin technology for fault detection in buildings

Haidar Hosamo Hosamo*, Henrik Kofoed Nielsen, Ammar Njeeb Alnmr, Paul Ragnar Svennevig, Kjeld Svidt

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

13 Citations (Scopus)
72 Downloads (Pure)

Abstract

This study aims to evaluate the utilization of technology known as Digital Twin for fault detection in buildings. The strategy consisted of studying existing applications, difficulties, and possibilities that come with it. The Digital Twin technology is one of the most intriguing newly discovered technologies rapidly evolving; however, some problems still need to be addressed. First, using Digital Twins to detect building faults to prevent future failures and cutting overall costs by improving building maintenance is still ambiguous. Second, how Digital Twin technology may be applied to discover inefficiencies inside the building to optimize energy usage is not well defined. To address these issues, we reviewed 326 documents related to Digital Twin, BIM, and fault detection in civil engineering. Then out of the 326 documents, we reviewed 115 documents related to Digital Twin for fault detection in detail. This study used a qualitative assessment to uncover Digital Twin technology’s full fault detection capabilities. Our research concludes that Digital Twins need more development in areas such as scanner hardware and software, detection and prediction algorithms, modeling, and twinning programs before they will be convincing enough for fault detection and prediction. In addition, more building owners, architects, and engineers need substantial financial incentives to invest in condition monitoring before many of the strategies discussed in the reviewed papers will be used in the construction industry. For future investigation, more research needs to be devoted to exploring how machine learning may be integrated with other Digital Twin components to develop new fault detection methods.

Original languageEnglish
Article number1013196
JournalFrontiers in Built Environment
Volume8
ISSN2297-3362
DOIs
Publication statusPublished - 9 Nov 2022

Bibliographical note

Publisher Copyright:
Copyright © 2022 Hosamo, Nielsen, Alnmr, Svennevig and Svidt.

Keywords

  • BIM
  • Digital twin
  • Fault detection
  • IoT
  • Predictive maintenance

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