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 advanced analytical tools such as Neural Networks, Machine Learning, Deep Learning, and expert systems.
“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]
The Introduction and §5.1 (page unavailable) describe machine learning and convolutional neural networks in construction-safety monitoring, supporting the claim that AI methods can assist digital-twin applications. The passages do not identify expert systems as such a tool, so the full list attributed to the review is not supported. (LLM, conf=0.84)
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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 · see citing works
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Real-time monitoring of construction sites: Sensors, methods, and applications
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Digital twin for smart manufacturing, A review
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Development of BIM, IoT and AR/VR technologies for fire safety and upskilling
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Real-time mixed reality-based visual warning for construction workforce safety
Wu, S., Hou, L., Zhang, G., & Chen, H. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104252 · Cited by 189 · see citing works
An investigation for integration of deep learning and digital twins towards Construction 4.0
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An Adapted Model of Cognitive Digital Twins for Building Lifecycle Management
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A digital twin approach for tunnel construction safety early warning and management
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Digital twins in the built environment: Definition, applications, and challenges
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Drivers for Digital Twin Adoption in the Construction Industry: A Systematic Literature Review
Opoku, D.-G. J., Perera, S., Osei‐Kyei, R., Rashidi, M., Famakinwa, T., & Bamdad, K. (2022) · Buildings · doi:10.3390/buildings12020113 · Cited by 134 · see citing works
A Parametric Scan-to-FEM Framework for the Digital Twin Generation of Historic Masonry Structures
Funari, . F., Hajjat, A. E., Masciotta, M. G., Oliveira, D. V., & Lourenço, P. B. (2021) · Sustainability · doi:10.3390/su131911088 · Cited by 131 · see citing works
Khan, A. A., Sepasgozar, S. M. E., Liu, T., & Yu, R. (2021) · Buildings · doi:10.3390/buildings11030126 · Cited by 129 · see citing works
Digital twin-supported smart city: Status, challenges and future research directions
Wang, H., Chen, X., Jia, F., & Cheng, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119531 · Cited by 135 · see citing works
Dodampegama, S., Hou, L., Asadi, E., Zhang, G., & Setunge, S. (2023) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2023.107375 · Cited by 131 · see citing works
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RetractedRETRACTED: Electromagnetic absorption of copper fiber oriented composite using 3D printing
Sun, J., Aslani, F., Wei, J., & Wang, X. (2021) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2021.124026 · Cited by 100 · see citing works
RetractedA Systematic Review of the Digital Transformation of the Building Construction Industry
Naji, K. K., Gündüz, M., Alhenzab, F. H., Al-Hababi, H., & Al-Qahtani, A. (2024) · IEEE Access · doi:10.1109/access.2024.3365934 · Cited by 114 · see citing works
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A Review of Digital Twin Applications in Civil and Infrastructure Emergency Management
Cheng, R., Hou, L., & Xu, S. (2023) · Buildings · doi:10.3390/buildings13051143 · Cited by 94 · see citing works
Digital Post-Disaster Risk Management Twinning: A Review and Improved Conceptual Framework
Lagap, U., & Ghaffarian, S. (2024) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2024.104629 · Cited by 82 · see citing works
Generative Design in Building Information Modelling (BIM): Approaches and Requirements
Ma, W., Wang, X., Wang, J., Xiang, X., & Sun, J. (2021) · Sensors · doi:10.3390/s21165439 · Cited by 88 · see citing works
Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review
Hodavand, F., Ramaji, I. J., & Sadeghi, N. (2023) · Buildings · doi:10.3390/buildings13061426 · Cited by 93 · see citing works
Digital twin: Stability analysis for tower crane hoisting safety with a scale model
Jiang, W., Ding, L., & Zhou, C. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104257 · Cited by 84 · see citing works
Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review
Hou, L., Chen, H., Zhang, G. (., & Wang, X. (2021) · Applied Sciences · doi:10.3390/app11020821 · Cited by 76 · see citing works
Self-citationA Modular Digital Twinning Framework for Safety Assurance of Collaborative Robotics
Douthwaite, J. A., Lesage, B., Gleirscher, M., Calinescu, R., Aitken, J. M., Alexander, R., & Law, J. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.758099 · Cited by 69 · see citing works
Umeokafor, N. I., Umar, T., & Evangelinos, K. I. (2022) · Safety Science · doi:10.1016/j.ssci.2022.105897 · Cited by 84 · see citing works
Digital Twins for Construction Assets Using BIM Standard Specifications
El-Din, M. N., Pereira, P. F., Martins, J. P., & Ramos, N. (2022) · Buildings · doi:10.3390/buildings12122155 · Cited by 80 · see citing works
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A comprehensive survey of digital twins: Applications, technologies and security challenges
Jeremiah, S. R., Azzaoui, A. E., Xiong, N. N., & Park, J. H. (2024) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2024.103120 · Cited by 77 · see citing works
Digital Twin Framework for Enabling Serial Construction
Kosse, S., Vogt, O., Wolf, M., König, M., & Gerhard, D. (2022) · Frontiers in Built Environment · doi:10.3389/fbuil.2022.864722 · Cited by 53 · see citing works
Construction and Maintenance of Building Geometric Digital Twins: State of the Art Review
Drobnyi, V., Hu, Z., Fathy, Y., & Brilakis, I. (2023) · Sensors · doi:10.3390/s23094382 · Cited by 61 · see citing works
Digital Twin-Based Intelligent Safety Risks Prediction of Prefabricated Construction Hoisting
Liu, Z., Meng, X., Xing, Z., Cao, C., Jiao, Y.-Y., & Li, A.-X. (2022) · Sustainability · doi:10.3390/su14095179 · Cited by 62 · see citing works
Afzal, M., Li, R. Y. M., Shoaib, M., Ayyub, M. F., Tagliabue, L. C., Bilal, M., Ghafoor, H., & Manta, O. P. (2023) · Sustainability · doi:10.3390/su152316436 · Cited by 71 · see citing works
How Digital Twin Concept Supports Internal Transport Systems?—Literature Review
Kosacka-Olejnik, M., Kostrzewski, M., Marczewska, M., Mrówczyńska, B., & Pawlewski, P. (2021) · Energies · doi:10.3390/en14164919 · Cited by 59 · see citing works
Cognitive ergonomics-based Augmented Reality application for construction performance
Wu, S., Hou, L., Chen, H., Zhang, G., Zou, Y., & Tushar, Q. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104802 · Cited by 59 · see citing works
Review of Digital Twins for Constructed Facilities
Khallaf, R., Khallaf, L., Anumba, C., & Madubuike, O. C. (2022) · Buildings · doi:10.3390/buildings12112029 · Cited by 64 · see citing works
Construction digital twin: a taxonomy and analysis of the application-technology-data triad
Saif, W., RazaviAlavi, S., & Kassem, M. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105715 · Cited by 64 · see citing works
Chen, H., Hou, L., Zhang, G., & Wu, S. (2022) · Safety Science · doi:10.1016/j.ssci.2022.105958 · Cited by 52 · see citing works
Wei, Y., Lei, Z., & Altaf, M. S. (2022) · Buildings · doi:10.3390/buildings12050566 · Cited by 53 · see citing works
A Bibliometric Analysis of Digital Twin in the Supply Chain
Siew, L. W., Hoe, L. W., & Lee, P. F. (2023) · Mathematics · doi:10.3390/math11153350 · Cited by 57 · see citing works
Porsani, G. B., Casquero-Modrego, N., Trueba, J. B. E., & Bandera, C. F. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113322 · Cited by 50 · see citing works
Gong, P., Lu, Y., Lovreglio, R., Lv, X., & Chi, Z. (2024) · Safety Science · doi:10.1016/j.ssci.2024.106624 · Cited by 56 · see citing works
Manzoor, B., Othman, I., Torres, J. C. P., & Chong, H. (2021) · Applied Sciences · doi:10.3390/app11188359 · Cited by 50 · see citing works
Engineering Brain: Metaverse for future engineering
Wang, X., Wang, J., Wu, C., Xu, S., & Ma, W. (2022) · AI in Civil Engineering · doi:10.1007/s43503-022-00001-z · Cited by 50 · see citing works
Geometric parameter updating in digital twin of built assets: A systematic literature review
Osadcha, I., Jurelionis, A., & Fokaides, P. A. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.106704 · Cited by 51 · see citing works
Park, J.-S., Lee, D.-G., Jimenez, J. A., Lee, S., & Kim, J. W. (2023) · Applied Sciences · doi:10.3390/app13074598 · Cited by 56 · see citing works
Zhang, M., Zhang, C., Zhang, J., Wang, L., & Wang, F. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2022.129940 · Cited by 43 · see citing works
Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study
Xu, S., Wang, J., Wu, P., Shou, W., Wang, X., & Chen, M. (2021) · Applied Sciences · doi:10.3390/app11073152 · Cited by 43 · see citing works
Fall Prevention From Ladders Utilizing a Deep Learning-Based Height Assessment Method
Anjum, S., Khan, N., Khalid, R., Khan, M., Lee, D., & Park, C. (2022) · IEEE Access · doi:10.1109/access.2022.3164676 · Cited by 42 · see citing works
Sun, J., Wang, Y., Li, K., Yao, X., Zhu, B., Wang, J., Dong, Q., & Wang, X. (2022) · Journal of Materials Research and Technology · doi:10.1016/j.jmrt.2022.05.061 · Cited by 39 · see citing works
Tracking the Research on Ten Emerging Digital Technologies in the AECO Industry
Dou, Y., Li, T., Li, L., Zhang, Y., & Li, Z. (2023) · Journal of Construction Engineering and Management · doi:10.1061/jcemd4.coeng-12290 · Cited by 40 · see citing works
Immersive Technologies-Driven Building Information Modeling (BIM) in the Context of Metaverse
Liu, Z., Gong, S., Tan, Z., & Demian, P. (2023) · Buildings · doi:10.3390/buildings13061559 · Cited by 39 · see citing works
Torrecilla-García, J. A., Pardo-Ferreira, M., & Rubio-Romero, J. (2021) · Dirección y Organización · doi:10.37610/dyo.v0i74.600 · Cited by 37 · see citing works
Impacts of digital twins on new business creation: insights from manufacturing industry
Timperi, M., Kokkonen, K., Hannola, L., & Elfvengren, K. (2023) · Measuring Business Excellence · doi:10.1108/mbe-09-2022-0104 · Cited by 14 · see citing works
Responsible cognitive digital clones as decision-makers: a design science research study
Golovianko, M., Gryshko, S., Terziyan, V. Y., & Tuunanen, T. (2022) · European Journal of Information Systems · doi:10.1080/0960085x.2022.2073278 · Cited by 37 · see citing works
Applications of existing and emerging construction safety technologies
Maali, O. N., Ko, C., & Nguyen, P. H. D. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105231 · Cited by 43 · see citing works
Naji, K. K., Gündüz, M., Alhenzab, F. H., Al-Hababi, H., & Al-Qahtani, A. (2024) · Buildings · doi:10.3390/buildings14030601 · Cited by 49 · see citing works
Ren, Z., Sun, J., Zeng, X., Chen, X., Wang, Y., Tang, W., & Wang, X. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2022.127650 · Cited by 34 · see citing works
Printable and Mechanical Performance of 3D Printed Concrete Employing Multiple Industrial Wastes
Wang, B., Mingang, Z., Yao, X., Wu, Q., Yang, M. G., Wang, X., Huang, J., & Zhao, H. (2022) · Buildings · doi:10.3390/buildings12030374 · Cited by 33 · see citing works
BIM, digital twin and cyber-physical systems: crossing and blurring boundaries
Douglas, D., Kelly, G., & Kassem, K. (2021) · Computing in construction · doi:10.35490/ec3.2021.196 · Cited by 32 · see citing works
Mechanical Performance of 3D Printed Concrete in Steam Curing Conditions
Wang, B., Yao, X., Yang, M. G., Zhang, R., Huang, J., Wang, X., Dong, Z., & Zhao, H. (2022) · Materials · doi:10.3390/ma15082864 · Cited by 32 · see citing works
Civil Infrastructure Digital Twins: Multi-Level Knowledge Map, Research Gaps, and Future Directions
Naderi, H., & Shojaei, A. (2022) · IEEE Access · doi:10.1109/access.2022.3223557 · Cited by 31 · see citing works
Manickam, S., Yarlagadda, L., Gopalan, S. P., & Chowdhary, C. L. (2023) · IEEE Access · doi:10.1109/access.2023.3338530 · Cited by 33 · see citing works
Zhang, G., Ding, Z., Zhang, R., Chen, C., Fu, G., Luo, X., Wang, Y., & Zhang, C. (2022) · Buildings · doi:10.3390/buildings12030350 · Cited by 31 · see citing works
Alhadi, A., Beach, T., & Yacine, R. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.111515 · Cited by 35 · see citing works
Extending the capability of component digital threads using material passports
Paramatmuni, C., & Cogswell, D. J. (2023) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2023.01.032 · Cited by 30 · see citing works
Using Eye-Tracking to Measure Worker Situation Awareness in Augmented Reality
Wu, S., Chen, H., Hou, L., Zhang, G., & Li, C. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105582 · Cited by 28 · see citing works
Alnaser, A. A., & Elmousalami, H. H. (2025) · Applied Sciences · doi:10.3390/app15094675 · Cited by 33 · see citing works
Harichandran, A., Johansen, K. W., Jacobsen, E. L., & Teizer, J. (2021) · International Symposium on Automation and Robotics in Construction (ISARC) · doi:10.22260/isarc2021/0084 · Cited by 26 · see citing works
Ellul, C., Hamilton, N., Pieri, A., & Floros, G. S. (2024) · Buildings · doi:10.3390/buildings14072216 · Cited by 25 · see citing works
Salzano, A., Cascone, S., Zitiello, E. P., & Nicolella, M. (2025) · Journal of Architectural Engineering · doi:10.1061/jaeied.aeeng-1855 · Cited by 29 · see citing works
A Parameter-Driven Method for Modeling Bridge Defects through IFC
Xu, S., Wang, J., Wang, X., Wu, P., Shou, W., & Liu, C. (2022) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0001026 · Cited by 25 · see citing works
A Bibliometrics-Based Systematic Review of Safety Risk Assessment for IBS Hoisting Construction
Junjia, Y., Alias, A. H., Haron, N. A., & Bakar, N. A. (2023) · Buildings · doi:10.3390/buildings13071853 · Cited by 27 · see citing works
Digital Twin in the Architecture, Engineering, and Construction Industry: A Bibliometric Review
Almatared, M., Liu, H.-X., Tang, S.-X., Sulaiman, M., Lei, Z., & Li, H.-X. (2022) · Construction Research Congress 2022 · doi:10.1061/9780784483961.070 · Cited by 28 · see citing works
Progress on Offshore Wind Farm Dynamic Wake Management for Energy
Zhao, L., Xue, L., Li, Z., Wang, J., Yang, Z., & Xue, Y. (2022) · Journal of Marine Science and Engineering · doi:10.3390/jmse10101395 · Cited by 24 · see citing works
Study on the Interaction Behaviors Identification of Construction Workers Based on ST-GCN and YOLO
Li, P., Wu, F., Xue, S., & Guo, L. (2023) · Sensors · doi:10.3390/s23146318 · Cited by 24 · see citing works
Intelligent Prediction of Prestressed Steel Structure Construction Safety Based on BP Neural Network
Zhu, H., & Wang, Y. (2022) · Applied Sciences · doi:10.3390/app12031442 · Cited by 24 · see citing works
Digital Twin-Enabled Infrastructures: A Bibliometric Analysis-Based Review
Taherkhani, R., Ashtari, M. A., & Aziminezhad, M. (2023) · Journal of Infrastructure Systems · doi:10.1061/jitse4.iseng-2323 · Cited by 25 · see citing works
Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0
Mon, B. F., Hayajneh, M., Ali, N. A., Ullah, F., Ullah, H., & Alkobaisi, S. (2025) · Computers, Materials & Continua · doi:10.32604/cmc.2025.061411 · Cited by 26 · see citing works
Salem, T. J., Dragomir, M., & Châtelet, Ė. (2024) · Applied Sciences · doi:10.3390/app14114787 · Cited by 22 · see citing works
Product digital twins: An umbrella review and research agenda for understanding their value
Medina, F. G., & Hernandez, V. M. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104181 · Cited by 23 · see citing works
Multi-objective optimisation for mortar containing activated waste glass powder
Sun, J., Long, Y., Xu, K., He, R. Y., Yao, X., Chen, M., Cai, T., Wang, X., & Wang, Y. (2022) · Journal of Materials Research and Technology · doi:10.1016/j.jmrt.2022.02.123 · Cited by 22 · see citing works
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Digital Twins and Civil Engineering Phases: Reorienting Adoption Strategies
Adebiyi, T. A., Ajenifuja, N. A., & Zhang, R. (2024) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4066181 · Cited by 22 · see citing works
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Digital-Twins towards Cyber-Physical Systems: A Brief Survey
Parnianifard, A., Jearavongtakul, S., Sasithong, P., Sinpan, N., Poomrittigul, S., Bajpai, A., Vanichchanunt, P., & Wuttisittikulkij, L. (2022) · Engineering Journal · doi:10.4186/ej.2022.26.9.47 · Cited by 17 · see citing works
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Zlatev, Z., & Dimov, I. (2022) · Atmosphere · doi:10.3390/atmos13060932 · Cited by 17 · see citing works
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A systematic review of occupational safety and health in modular integrated construction
Sadeghi, H., Cheung, C. M., Yunusa‐Kaltungo, A., & Manu, P. (2025) · Safety Science · doi:10.1016/j.ssci.2025.106897 · Cited by 16 · see citing works
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- 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
- Health Sciences › Health Professions › Radiological and Ultrasound Technology › 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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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.
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