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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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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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