The DOI matches its Crossref record; 115 of the 116 references it cites were found in a registry or an index.
Literature Review of Digital Twins Applications in Construction Workforce Safety
Open accessAbstract
For many decades, safety has been a challenge in the construction sector. Despite extensive efforts to improve overall safety, the sector’s casualty rate still remains high. In practice, dynamic and complex construction processes may lead to on-site risks and safety plans being overlooked, likely leading to a variety of safety accidents. Nowadays, under the guidance of the digital twins (DT) concept, the advent of state-of-the-art sensing and visualisation technologies has offered the possibility to improve construction health and safety in the workplace. To understand the research advances of these technologies, identify their gaps and challenges, and propose solutions to further advance the industry’s safety, we conducted and report a thorough review on the state-of-the-art technological studies, and elaborate upon the key findings in detail. For instance, despite DT being proven to be effective in improving construction workforce safety, the construction industry has yet to fully exploit and streamline these innovations in practice. Overall, this review provides insights into technological clustering, improvement strategies, as well as workforce safety, which can benefit from formulating effective digital technology paradigms.
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“AI has the potential to assist DTs through the utilization of advanced analytical tools such as Neural Networks (NN), Machine Learning (ML), Deep Learning (DL), and expert systems [50].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [50]
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Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Towards a semantic Construction Digital Twin: Directions for future research
Boje, C., Guerriero, A., Kubicki, S., & Rezgui, Y. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103179 · Cited by 1,477
Shaping the digital twin for design and production engineering
Schleich, B., Anwer, N., Mathieu, L., & Wartzack, S. (2017) · CIRP Annals · doi:10.1016/j.cirp.2017.04.040 · Cited by 1,305
Digital Twin—The Simulation Aspect
Boschert, S., & Rosen, R. (2016) · Mechatronic Futures · doi:10.1007/978-3-319-32156-1_5 · Cited by 1,158
Reengineering aircraft structural life prediction using a digital twin
Tuegel, E. J., Ingraffea, A. R., Eason, T. G., & Spottswood, S. M. (2011) · International Journal of Aerospace Engineering · doi:10.1155/2011/154798 · Cited by 1,119
A critical review of virtual and augmented reality (VR/AR) applications in construction safety
Li, X., Yi, W., Chi, H.-L., Wang, X., & Chan, A. P. C. (2017) · Automation in Construction · doi:10.1016/j.autcon.2017.11.003 · Cited by 1,063
A critical review of the use of 3-D printing in the construction industry
Wu, P., Wang, J., & Wang, X. (2016) · Automation in Construction · doi:10.1016/j.autcon.2016.04.005 · Cited by 1,009
Zhang, S., Teizer, J., Lee, J.-K., Eastman, C. M., & Venugopal, M. (2012) · Automation in Construction · doi:10.1016/j.autcon.2012.05.006 · Cited by 838
Stakeholder management studies in mega construction projects: A review and future directions
Mok, K. Y., Shen, Q., & Yang, J. (2014) · International Journal of Project Management · doi:10.1016/j.ijproman.2014.08.007 · Cited by 600
Batty, M. (2018) · Environment and Planning B: Urban Analytics and City Science · doi:10.1177/2399808318796416 · Cited by 551
Cited by
Sorted by citations
Digital twin: benefits, use cases, challenges, and opportunities
Attaran, M., & Celik, B. G. (2023) · Decision Analytics Journal · doi:10.1016/j.dajour.2023.100165 · Cited by 704
Borowski, P. (2021) · Energies · doi:10.3390/en14071885 · Cited by 449
Real-time monitoring of construction sites: Sensors, methods, and applications
Rao, A. S., Radanović, M., Liu, Y., Hu, S., Fang, Y., Khoshelham, K., Palaniswami, M. S., & Ngo, T. (2022) · Automation in Construction · doi:10.1016/j.autcon.2021.104099 · Cited by 452
Digital twin for smart manufacturing, A review
Soori, M., Behrooz Arezoo, B., & Dastres, R. (2023) · Sustainable Manufacturing and Service Economics · doi:10.1016/j.smse.2023.100017 · Cited by 411
Omrany, H., Al-Obaidi, K. M., Husain, A., & Ghaffarianhoseini, A. (2023) · Sustainability · doi:10.3390/su151410908 · Cited by 332
Metadata
- Authors
- Hou, L., Wu, S., Zhang, G. (., Tan, Y., & Wang, X.
- Affiliations
- RMIT University (AU)
- Tongji University (CN)
- Corresponding author
- Wang, Xiangyu
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2020-12-31
- Volume
- 11
- Issue
- 1
- Pages
- 339
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W3116566930
- Cited by
- 194
- References
- 116
- Citations by year
- 2026: 13 · 2025: 23 · 2024: 47 · 2023: 49 · 2022: 43 · 2021: 19
- FWCI
- 26.46
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- RMIT Research Repository (RMIT University Library) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Topics
- Social Sciences › Safety Research › Occupational Health and Safety Research
- Physical Sciences › Engineering › Building and Construction › BIM and Construction Integration
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- digital twin, construction safety, construction accidents, sensor technology, safety management
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