TY - JOUR
T1 - A review of the Digital Twin technology for fault detection in buildings
AU - Hosamo, Haidar Hosamo
AU - Nielsen, Henrik Kofoed
AU - Alnmr, Ammar Njeeb
AU - Svennevig, Paul Ragnar
AU - Svidt, Kjeld
N1 - Publisher Copyright:
Copyright © 2022 Hosamo, Nielsen, Alnmr, Svennevig and Svidt.
PY - 2022/11/9
Y1 - 2022/11/9
N2 - 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.
AB - 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.
KW - BIM
KW - Digital twin
KW - Fault detection
KW - IoT
KW - Predictive maintenance
KW - BIM
KW - Digital twin
KW - Fault detection
KW - IoT
KW - Predictive maintenance
UR - http://www.scopus.com/inward/record.url?scp=85142427747&partnerID=8YFLogxK
U2 - 10.3389/fbuil.2022.1013196
DO - 10.3389/fbuil.2022.1013196
M3 - Review article
AN - SCOPUS:85142427747
SN - 2297-3362
VL - 8
JO - Frontiers in Built Environment
JF - Frontiers in Built Environment
M1 - 1013196
ER -