Volume 12 • Issue 2 • PP: 32–38 • 2026
BIM-Integrated Semantic Risk Intelligence for Construction Safety Severity Prediction Using Incident Narratives and 4D Work-Zone Attributes
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Construction safety management increasingly depends on the ability to connect static building information models with dynamic evidence from site operations. This paper proposes a BIM-integrated semantic risk intelligence model that translates accident narratives into work-zone risk indicators and uses them to infer safety severity. The model links textual incident evidence with BIM-relevant descriptors, including construction phase, spatial zone, temporary protection status, energy isolation, and proximity to safety constraints. A formal risk-scoring layer is combined with supervised severity learning to provide interpretable decision support for safety planning and 4D coordination. The study contributes a reproducible methodology for converting unstructured safety reports into BIM-actionable risk representations, supporting early hazard prioritisation, design-for-safety review, and site control planning. The findings indicate that semantic evidence becomes more useful when it is explicitly fused with BIM phase and spatial context, rather than being treated as disconnected textual data.
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References
[1] W. H. Collinge, K. Farghaly, M. Hadi Mosleh, P. Manu, and C. A. Osorio-Sandoval, “BIM-based construction safety risk library,” Automation in Construction, vol. 141, p. 104391, 2022.
[2] Y. Lu, P. Gong, Y. Tang, S. Sun, and Q. Li, “BIMintegrated construction safety risk assessment at the design stage of building projects,” Automation in Construction, vol. 124, p. 103553, 2021.
[3] B. Li, C. Schultz, J. Teizer, O. Golovina, and J. Melzner, “Towards a unifying domain model of construction safety, health and well-being: SafeConDM,” Advanced Engineering Informatics, vol. 51, p. 101487, 2022.
[4] B. Yang, B. Zhang, Q. Zhang, Z. Wang, M. Dong, and T. Fang, “Automatic detection of falling hazard from surveillance videos based on computer vision and building information modeling,” Structure and Infrastructure Engineering, 2022.
[5] W. H. Collinge and C. A. Osorio-Sandoval, “Deploying a Building Information Modelling (BIM)-based construction safety risk library for industry: Lessons learned and future directions,” Buildings, vol. 14, no. 2, p. 500, 2024.
[6] A. Salzano, F. Longo, L. Nicoletti, and A. Padovano, “Construction safety and efficiency: Integrating Building Information Modeling for advanced safety management,” Sustainability, vol. 16, no. 10, p. 4094, 2024.
[7] A. A. U. Zaman, M. M. Rahman, and M. R. Hossain, “Integration of BIM data and real-time game engine applications for construction safety,” Journal of Information Technology in Construction, vol. 29, pp. 134–159, 2024.
[8] Y. Jiang, H. Zhang, and L. Ding, “Applications of digital twin technology in construction safety risk management,” Engineering, Construction and Architectural Management, 2024.
[9] D. Kim, T. Yoo, S. V.-T. Tran, D. Lee, C. Park, and D. Lee, “Automated safety risk assessment framework by integrating safety regulation and 4D BIM-based rule modeling,” Buildings, vol. 14, no. 8, p. 2529, 2024.
[10] M. Parsamehr and R. Ruparathna, “A BIM-based twostage fuzzy inference system for safety risk prediction in building construction projects,” Canadian Journal of Civil Engineering, vol. 50, no. 1, pp. 11–23, 2023.
[11] W. Saif, T. Williams, C. Wong, J. Dobos, P. Martinez Rodriguez, and M. Kassem, “Digital twin for safety on construction sites: A real-time risk monitoring system combining wearable sensors and 4D BIM,” in Proceedings of the 2024 European Conference on Computing in Construction, pp. 526–533, 2024.
[12] W. Jiang, L. Ding, and C. Zhou, “Digital twin: Stability analysis for tower crane hoisting safety with a scale model,” Automation in Construction, 2022.
[13] A. J.-P. Tixier, M. R. Hallowell, B. Rajagopalan, and D. Bowman, “Automated content analysis for construction safety: A natural language processing system to extract precursors and outcomes from unstructured injury reports,” Automation in Construction, vol. 62, pp. 45–56, 2016.
[14] J. Yuan, X. Li, X. Xiahou, N. Tymvios, Z. Zhou, and Q. Li, “Accident prevention through design (PtD): Integration of building information modeling and PtD knowledge base,” Automation in Construction, vol. 102, pp. 86–104, 2019.
[15] S. Zhang, K. Sulankivi, M. Kiviniemi, I. Romo, C. M. Eastman, and J. Teizer, “BIM-based fall hazard identification and prevention in construction safety planning,” Safety Science, vol. 72, pp. 31–45, 2015.
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