Transforming Occupational Health and Safety under Saudi Vision 2030: A Strategic Framework for Predictive, Digital and Resilient Workplaces

References

[1] S. V. 2030, Saudi Vision 2030: A vibrant society, a thriving economy and an ambitious nation. Kingdom of Saudi Arabia, 2026.

[2] S. Abdul Salam, “From Exposure to Action: Organizational Antecedents of Longitudinal Occupational-Exposure Management in Construction and the Development of a Worker Exposure Profile-Based COEMF,” unpublished manuscript, n.d.

[3] T. Ahmad, M. Zaid, and M. H. Hussain, “IMPACT OF DIGITAL TECHNOLOGIES (IOT/AI) ON WORKPLACE SAFETY IN SAUDI INDUSTRIES,” Veredas Do Direito Direito Ambiental e Desenvolvimento Sustentável, vol. 23, 2026.

[4] A. Akinsemoyin, S. Langar, and I. Awolusi, “Framework for UAV-BIM Integration for Proactive Hazard Identification in Construction,” pp. 697–706, 2024. ASCE

[5] M. M. Alateeq, F. R. P. P., and M. A. S. Ali, “Construction Site Hazards Identification Using Deep Learning and Computer Vision,” Sustainability, vol. 15, no. 3, Art. no. 2358, 2023. MDPI

[6] S. Alizadehsalehi, İ. Yitmen, T. Çelik, and D. Arditi, “The effectiveness of an integrated BIM/UAV model in managing safety on construction sites,” International Journal of Occupational Safety and Ergonomics, vol. 26, no. 4, pp. 829–844, 2018. Taylor & Francis

[7] S. Alsulamy, M. Alshayeb, I. Inam, and A. Ahmed, “Machine learning applications for predicting safety incidents in construction industry,” Scientific Reports, vol. 16, no. 1, Art. no. 4673, 2026. Nature

[8] I. Awolusi, E. Marks, and M. R. Hallowell, “Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices,” Automation in Construction, vol. 85, pp. 96–106, 2018. ScienceDirect

[9] A. Babalola, P. Urhal, P. Manu, P. Bártolo, C. M. Cheung, A. Yunusa‐Kaltungo, C. S. R. Perera, S. Gao, V. Francis, and V. Paton-Cole, “A systematic review of Internet of Things applications in construction occupational safety and health management,” UWE Research Repository (UWE Bristol), pp. 283–295, 2023.

[10] Y. Balgheeth, “Enhancing existing health and safety processes in public sector construction projects within Saudi Arabia using building information modelling approaches,” in University of Salford Institutional Repository (University of Salford). University of Salford, 2016.

[11] S. T. Bennett, W. Han, D. Mahmud, P. G. Adamczyk, F. Dai, M. Wehner, D. Veeramani, and Z. Zhu, “Usability and Biomechanical Testing of Passive Exoskeletons for Construction Workers: A Field-Based Pilot Study,” Buildings, vol. 13, no. 3, Art. no. 822, 2023. MDPI

[12] H. Chen, Y. Mao, Y. Xu, and R. Wang, “The Impact of Wearable Devices on the Construction Safety of Building Workers: A Systematic Review,” Sustainability, vol. 15, no. 14, Art. no. 11165, 2023. MDPI

[13] K. Chen, “Enhancing construction safety management through edge computing: Framework and scenarios,” Journal of Information Technology in Construction, vol. 25, pp. 438–451, 2020. ITcon

[14] J. E. Dodoo, H. Al‐Samarraie, A. I. Alzahrani, and T. Tang, “XR and Workers’ safety in High-Risk Industries: A comprehensive review,” Safety Science, vol. 185, Art. no. 106804, 2025. ScienceDirect

[15] Y. Ding, M. Zhang, J. Pan, J. Hu, and X. Luo, “Robust object detection in extreme construction conditions,” Automation in Construction, vol. 165, Art. no. 105487, 2024. ScienceDirect

[16] F. Elghaish, S. Matarneh, S. Talebi, M. Kagioglou, M. R. Hosseini, and S. Abrishami, “Toward digitalization in the construction industry with immersive and drones technologies: a critical literature review,” Huddersfield Research Portal (University of Huddersfield), vol. 10, no. 3, pp. 345–363, 2020.

[17] E. D. O. Erkal, M. R. Hallowell, A. Ghriss, and S. Bhandari, “Predicting Serious Injury and Fatality Exposure Using Machine Learning in Construction Projects,” Journal of Construction Engineering and Management, vol. 150, no. 3, 2023.

[18] M. Gangolells, M. Casals, N. Forcada, X. Roca, and A. Fuertes, “Mitigating construction safety risks using prevention through design,” Journal of Safety Research, vol. 41, no. 2, pp. 107–122, 2010. ScienceDirect

[19] S. Gyawali, A. Mohammadjafari, S. Ghimire, and M. Habibnezhad, “Reliable vision-based PPE detection for construction safety in adverse environmental conditions,” Buildings, vol. 16, no. 12, Art. no. 2447, 2026. MDPI

[20] M. R. Hallowell, J. W. Hinze, K. C. Baud, and A. Wehle, “Proactive Construction Safety Control: Measuring, Monitoring, and Responding to Safety Leading Indicators,” Journal of Construction Engineering and Management, vol. 139, no. 10, 2013. ASCE

[21] J. W. Hinze, S. T. Thurman, and A. Wehle, “Leading indicators of construction safety performance,” Safety Science, vol. 51, no. 1, pp. 23–28, 2013. ScienceDirect

[22] E. Hollnagel, Safety-II in practice; Developing the resilience potentials. In University of Southern Denmark Research Portal (University of Southern Denmark). University of Southern Denmark, 2018.

[23] L. Hou, S. Wu, G. Zhang, Y. Tan, and X. Wang, “Literature Review of Digital Twins Applications in Construction Workforce Safety,” Applied Sciences, vol. 11, no. 1, Art. no. 339, 2020. MDPI

[24] International Labour Organization (ILO), NORMLEX country profile and ratifications for Saudi Arabia. International Labour Organization, 2026a.

[25] International Electrotechnical Commission, IEC 60529:1989+AMD1:1999+AMD2:2013, Degrees of protection provided by enclosures (IP Code). IEC, 2013.

[26] Z. Jin, J. Gambatese, A. A. Karakhan, and C. A. Nnaji, “Analysis of prevention through design studies in construction: A subject review,” Journal of Safety Research, vol. 84, pp. 138–154, 2022. ScienceDirect

[27] J. G. Kamal, A. T. Azar, M. Alkanhal, and N. Ahmad, “AUTOMATED HAZARD DETECTION AND SAFETY MONITORING FOR SMART CONSTRUCTION USING DEEP LEARNING ALGORITHM,” Zenodo (CERN European Organization for Nuclear Research), 2026.

[28] J. Kim, K. Lee, and J. Jeon, “Systematic literature review of wearable devices and data analytics for construction safety and health,” Expert Systems with Applications, vol. 257, Art. no. 125038, 2024. ScienceDirect

[29] S. Kim, A. Moore, D. Srinivasan, A. Akanmu, A. Barr, C. Harris‐Adamson, D. Rempel, and M. A. Nussbaum, “Potential of Exoskeleton Technologies to Enhance Safety, Health, and Performance in Construction: Industry Perspectives and Future Research Directions,” IISE Transactions on Occupational Ergonomics and Human Factors, vol. 7, pp. 185–191, 2019. Taylor & Francis

[30] C.-M. Lee and S.-H. Hsieh, “Edge-enabled real-time framework for context-aware data acquisition and hazard alerting in smart construction sites,” Automation in Construction, vol. 181, Art. no. 106642, 2026. ScienceDirect

[31] S. Li, Q.-C. Wang, H.-H. Wei, and J. Chen, “Extended Reality (XR) Training in the Construction Industry: A Content Review,” Buildings, vol. 14, no. 2, Art. no. 414, 2024. MDPI

[32] T. Linner, M. Pan, W. L. Pan, M. Taghavi, W. Pan, and T. Böck, “Identification of Usage Scenarios for Robotic Exoskeletons in the Context of the Hong Kong Construction Industry,” Proceedings of the ... ISARC, 2018. ISARC

[33] Q. Luo, C. Sun, Y. Li, Z. Qi, and G. Zhang, “Applications of digital twin technology in construction safety risk management: a literature review,” Engineering Construction & Architectural Management, vol. 32, no. 6, pp. 3587–3607, 2024. Emerald

[34] A. S. Mahmoud, M. A. Hassanain, A. Istanbullu, V. O. Otitolaiye, F. Omer, M. O. Sanni-Anibire, and Y. A. Dodo, “Perceived Importance of Construction Safety Performance Factors and Industry 4.0 Safety Technologies in the Saudi Arabian Construction Industry: An Exploratory Assessment,” Buildings, vol. 16, no. 16, Art. no. 3193, 2026. MDPI

[35] A. A. Makki and I. Mosly, “Determinants for Safety Climate Evaluation of Construction Industry Sites in Saudi Arabia,” International Journal of Environmental Research and Public Health, vol. 17, no. 21, Art. no. 8225, 2020. MDPI

[36] A. A. Makki and I. Mosly, “Predicting the Safety Climate in Construction Sites of Saudi Arabia: A Bootstrapped Multiple Ordinal Logistic Regression Modeling Approach,” Applied Sciences, vol. 11, no. 4, Art. no. 1474, 2021. MDPI

[37] B. Manzoor, T. Zayed, G. Alfalah, M. F. A.- Afari, A. H. Qureshi, A. Waheed, and P. Manu, “Blockchain and digital twin integration for construction safety: A bibliometric and systematic review,” Journal of Safety Science and Resilience, Art. no. 100302, 2026. ScienceDirect

[38] T. Y. Melesse, M. Braggio, M. S. Peer, M. Mosca, I. Sacchi, S. Arena, and F. Briatore, “Workplace Safety Enhancement through Industry 4.0 Adoption,” Procedia Computer Science, vol. 277, pp. 3645–3654, 2026. ScienceDirect

[39] Ministry of Human Resources and Social Development (MHRSD), Guideline for occupational safety and health hazards and how to deal with them. Kingdom of Saudi Arabia, 2022.

[40] A. A. Mohammed, S. S. Mohammed, A. A. Mohammed, and S. A. Kamran, “AI-AUGMENTED SAFETY MANAGEMENT IN CONSTRUCTION,” International Journal of Computer Science and Information Technology, vol. 17, no. 5, p. 107, 2025.

[41] A. A. Mohy, H. Bassioni, E. O. Elgendi, and T. M. Hassan, “Innovations in safety management for construction sites: the role of deep learning and computer vision techniques,” Construction Innovation, vol. 26, no. 2, pp. 551–578, 2024.

[42] A. A. Mohy, H. Bassioni, E. O. Elgendi, and T. M. Hassan, “Deep Learning Enabled Computer Vision Model for Automated Safety Compliance in Construction Environments,” Journal of Information Technology in Construction, vol. 30, no. 1, p. 1398, 2025. ITcon

[43] I. Mosly, “Barriers, Enablers, and Adoption Patterns of IoT and Wearable Devices in the Saudi Construction Industry: Survey Evidence,” Buildings, vol. 16, no. 2, Art. no. 347, 2026. MDPI

[44] I. Mosly and A. A. Makki, “Safety Climate Perceptions in the Construction Industry of Saudi Arabia: The Current Situation,” International Journal of Environmental Research and Public Health, vol. 17, no. 18, Art. no. 6717, 2020. MDPI

[45] National Council for Occupational Safety and Health (NCOSH), Council overview and strategic direction. Kingdom of Saudi Arabia, 2026b.

[46] National Council for Occupational Safety and Health (NCOSH), Occupational safety and health rules, regulations and national policy resources. Kingdom of Saudi Arabia, 2026c.

[47] C. Okonkwo, I. Awolusi, C. A. Nnaji, and O. Akanfe, “Privacy and security of wearable internet of things: A scoping review and conceptual framework development for safety and health management in construction,” Computers & Security, vol. 150, Art. no. 104275, 2024.

[48] M. Oladele, M. G. Jeong, and Y. Jung, “Classification of Fatal and Non-fatal Construction Incidents in the Southeastern U.S. using Machine Learning,” EPiC Series in Built Environment, vol. 6, pp. 856–845, 2025.

[49] International Labour Organization (ILO), Saudi Arabia ratifies the Promotional Framework for Occupational Safety and Health Convention, 2006 (No. 187). International Labour Organization, 2024.

[50] S. Park, K. Jeong, J. Kim, H. Kim, S. Yoon, J. Kim, and J. Jeon, “Comparative evaluation of machine learning and LLM-based approaches for construction accident prediction,” Journal of Asian Architecture and Building Engineering, pp. 1–16, 2026. Taylor & Francis

[51] K. Radman, M. B. Jelodar, R. Lovreglio, E. Ghazizadeh, and S. Wilkinson, “Digital technologies and data-driven delay management process for construction projects,” Frontiers in Built Environment, vol. 8, 2022. Frontiers

[52] R. Sacks, A. Perlman, and R. Barak, “Construction safety training using immersive virtual reality,” Construction Management and Economics, vol. 31, no. 9, pp. 1005–1017, 2013. Taylor & Francis

[53] D. Salinas, F. M.-L. Rivera, and J. M. Serrano, “Critical Analysis of the Evaluation Methods of Extended Reality (XR) Experiences for Construction Safety,” International Journal of Environmental Research and Public Health, vol. 19, no. 22, Art. no. 15272, 2022. PubMed

[54] N. S. Samsudin, M. Z. Mohammad, N. Khalil, N. D. Nadzri, and C. K. I. C. Ibrahim, “A thematic review on Prevention through design (PtD) concept application in the construction industry of developing countries,” Safety Science, vol. 148, Art. no. 105640, 2021. ScienceDirect

[55] Saudi Vision 2030, National Transformation Program. Kingdom of Saudi Arabia, 2026d.

[56] D. G. Scorgie, Z. Feng, D. Paes, F. Parisi, T. W. Yiu, and R. Lovreglio, “Virtual reality for safety training: A systematic literature review and meta-analysis,” Safety Science, vol. 171, Art. no. 106372, 2023. ScienceDirect

[57] M. Shariatfar, A. Deria, and Y.-C. Lee, “Digital Twin in Construction Safety and Its Implications for Automated Monitoring and Management,” Construction Research Congress 2022, pp. 591–600, 2022. ASCE

[58] J. Teizer, K. W. Johansen, and C. Schultz, “The Concept of Digital Twin for Construction Safety,” Construction Research Congress 2022, pp. 1156–1165, 2022. ASCE

[59] A. J. ‐P. Tixier, M. R. Hallowell, B. Rajagopalan, and D. Bowman, “Application of machine learning to construction injury prediction,” vol. 69, pp. 102–114, 2016. ScienceDirect

[60] C. Turner, J. Oyekan, L. Stergioulas, and D. Griffin, “Utilizing Industry 4.0 on the Construction Site: Challenges and Opportunities,” IEEE Transactions on Industrial Informatics, vol. 17, no. 2, pp. 746–756, 2020. IEEE

[61] J. Xu, C. M. Cheung, P. Manu, and O. Ejohwomu, “Safety leading indicators in construction: A systematic review,” Safety Science, vol. 139, Art. no. 105250, 2021. ScienceDirect

[62] D. M. Zohar, “Safety climate in industrial organizations: Theoretical and applied implications,” Journal of Applied Psychology, vol. 65, no. 1, pp. 96–102, 1980. APA

[63] S. Abdul Salam, “Cumulative Occupational Exposure Management Executive Survey Analysis and Strategic Framework for Construction Projects,” unpublished practitioner survey and analysis, n.d.

@article{1723472,
      author = {Shajahan Abdul Salam},
      title = {Transforming Occupational Health and Safety under Saudi Vision 2030: A Strategic Framework for Predictive, Digital and Resilient Workplaces},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {8},
      pages = {2637-2675},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723472.pdf},
      abstract = {Saudi Vision 2030 is reshaping the Kingdom's economy, infrastructure, labour market, and institutional capabilities. Rapid expansion in construction, industry, logistics, tourism, advanced manufacturing, and large-scale infrastructure generates a parallel requirement for occupational health and safety systems capable of moving beyond compliance-led and injury-reactive approaches. This paper develops the Saudi Vision 2030 Occupational Safety Transformation Framework (SV2030-OSTF), an applied conceptual framework for progressing toward predictive, digitally enabled, worker-centred, and resilient workplaces. The framework was developed through a structured narrative synthesis of official Saudi policy sources, International Labour Organization instruments, and peer-reviewed OSH literature, with particular attention to Saudi construction and high-hazard work. Seven mutually reinforcing dimensions are proposed: (1) governance and assurance; (2) predictive risk intelligence; (3) occupational health and exposure prevention; (4) human capability, leadership, and worker voice; (5) digital safety and connected worksites; (6) prevention through design and lifecycle safety; and (7) resilience, learning, and operational readiness. A maturity model, an indicator architecture, a technology-selection gate, a five-layer safety intelligence reference architecture, and a phased 2026–2030 implementation roadmap are also presented. The framework deliberately treats technology as an enabler rather than a substitute for management commitment, competent supervision, worker participation, and effective risk controls. Its principal contribution is to connect national transformation objectives with an operational OSH architecture that can be piloted at sector, organisation, and project levels. The framework is conceptual and requires empirical validation before use in benchmarking or regulatory decision-making.},
      keywords = {Computer vision; Digital safety; Occupational safety and health; Predictive safety; Saudi Vision 2030},
      month = {February},
      doi = {https://doi.org/10.64388/IREV9I8-1723472}
  }