Conceptual Modeling of Data-Driven Occupational Safety Risk Control in Large-Scale Energy Infrastructure Projects

References

[1] Agenda, I., 2015. Industrial internet of things: unleashing the potential of connected products and services. White Paper, in collaboration with Accenture, 34, p.40.

[2] Alstone, P.M., 2015. Connections beyond the margins of the power grid Information technology and the evolution of off-grid solar electricity in the developing world. University of California, Berkeley.

[3] Ani, U.P.D., He, H. and Tiwari, A., 2017. Review of cybersecurity issues in industrial critical infrastructure: manufacturing in perspective. Journal of Cyber Security Technology, 1(1), pp.32-74.

[4] Antonucci, D., 2017. The cyber risk handbook: Creating and measuring effective cybersecurity capabilities. John Wiley & Sons.

[5] Aziz, Z., Riaz, Z. and Arslan, M., 2017. Leveraging BIM and Big Data to deliver well maintained highways. Facilities, 35(13/14), pp.818-832.

[6] Baumer, E.P., Elovic, E., Qin, Y., Polletta, F. and Gay, G., 2015. Framing annotation data for news articles. North American Chapter of the Association for Computational Linguistics (NAACL).

[7] Beier, P., Hansen, L.J., Helbrecht, L. and Behar, D., 2017. A how‐to guide for coproduction of actionable science. Conservation Letters, 10(3), pp.288-296.

[8] Bello, A.U., 2017. Improving anti-money laundering compliance: Self-protecting theory and money laundering reporting officers. Springer.

[9] Bhanot, V., 2017. How to transform your bank into a data-driven organisation?. Journal of Digital Banking, 1(4), pp.296-308.

[10] Blowers, M., Iribarne, J., Colbert, E. and Kott, A., 2016. The future internet of things and security of its control systems. arXiv preprint arXiv:1610.01953.

[11] Bogard, K., Ludwig, T.D., Staats, C. and Kretschmer, D., 2015. An industry’s call to understand the contingencies involved in process safety: Normalization of deviance. Journal of Organizational Behavior Management, 35(1-2), pp.70-80.

[12] Bolton, R. and Foxon, T.J., 2015. Infrastructure transformation as a socio-technical process—Implications for the governance of energy distribution networks in the UK. Technological forecasting and social change, 90, pp.538-550.

[13] Boone, W. and McDougall, A., 2016. Governance and compliance. Handbook of SCADA/Control Systems Security, 201.

[14] Botha, P.S. and Scheepbouwer, E., 2015. Christchurch rebuild, New Zealand: alliancing with a difference. Proceedings of the Institution of Civil Engineers-Management, Procurement and Law, 168(3), pp.121-129.

[15] Broeders, D., Schrijvers, E., van der Sloot, B., Van Brakel, R., De Hoog, J. and Ballin, E.H., 2017. Big Data and security policies: Towards a framework for regulating the phases of analytics and use of Big Data. Computer Law & Security Review, 33(3), pp.309-323.

[16] Brown, J.P., 2016. The effect of automation on human factors in aviation. The Journal of Instrumentation, Automation and Systems, 3(2), pp.31-46.

[17] Burgass, M.J., Halpern, B.S., Nicholson, E. and Milner-Gulland, E.J., 2017. Navigating uncertainty in environmental composite indicators. Ecological Indicators, 75, pp.268-278.

[18] Carayon, P., Hancock, P., Leveson, N., Noy, I., Sznelwar, L. and Van Hootegem, G., 2015. Advancing a sociotechnical systems approach to workplace safety–developing the conceptual framework. Ergonomics, 58(4), pp.548-564.

[19] Cheng, X., Liao, S. and Hua, Z., 2017. A policy of picking up parcels for express courier service in dynamic environments. International Journal of Production Research, 55(9), pp.2470-2488.

[20] Chmutina, K., Bosher, L., Dainty, A., Sweijs, T., Mukena, J., Frinking, E., Lucini, B. and Lombardi, M., 2015. Case study on North-West Europe.

[21] Christensen, T., Lægreid, P. and Rykkja, L.H., 2016. Organizing for crisis management: Building governance capacity and legitimacy. Public administration review, 76(6), pp.887-897.

[22] Coppola, D.P., 2015. Hazards. Introduction to international disaster management, p.40.

[23] Cuppen, E., Bosch-Rekveldt, M.G., Pikaar, E. and Mehos, D.C., 2016. Stakeholder engagement in large-scale energy infrastructure projects: Revealing perspectives using Q methodology. International Journal of Project Management, 34(7), pp.1347-1359.

[24] Diamantoulakis, P.D., Kapinas, V.M. and Karagiannidis, G.K., 2015. Big data analytics for dynamic energy management in smart grids. Big Data Research, 2(3), pp.94-101.

[25] Drias, Z., Serhrouchni, A. and Vogel, O., 2015, August. Analysis of cyber security for industrial control systems. In 2015 international conference on cyber security of smart cities, industrial control system and communications (ssic) (pp. 1-8). IEEE.

[26] Ellis, S., Morris, H.D. and Santagate, J., 2015. IoT-enabled analytic applications revolutionize supply chain planning and execution. International Data Corporation (IDC) White Paper, 13, p.259697.

[27] Forteza, F.J., Carretero-Gómez, J.M. and Sesé, A., 2017. Effects of organizational complexity and resources on construction site risk. Journal of safety research, 62, pp.185-198.

[28] Fox, W.E., Medina-Cetina, Z., Angerer, J., Varela, P. and Chung, J.R., 2017. Water quality & natural resource management on military training lands in central Texas: Improved decision support via Bayesian networks. Sustainability of Water Quality and Ecology, 9, pp.39-52.

[29] Fraga-Lamas, P., Fernández-Caramés, T.M., Suárez-Albela, M., Castedo, L. and González-López, M., 2016. A review on internet of things for defense and public safety. Sensors, 16(10), p.1644.

[30] Fraher, A.L., Branicki, L.J. and Grint, K., 2017. Mindfulness in action: Discovering how US Navy Seals build capacity for mindfulness in high-reliability organizations (HROs). Academy of Management Discoveries, 3(3), pp.239-261.

[31] Gambatese, J., Gibb, A., Bust, P. and Behm, M., 2017. Expanding Prevention through Design (PtD) in practice: innovation, change, and a path forward. Journal of Construction Project Management and Innovation, 7(2), pp.1995-2006.

[32] Gnoni, M.G. and Saleh, J.H., 2017. Near-miss management systems and observability-in-depth: Handling safety incidents and accident precursors in light of safety principles. Safety science, 91, pp.154-167.

[33] Golden, S.H., Hager, D., Gould, L.J., Mathioudakis, N. and Pronovost, P.J., 2017. A gap analysis needs assessment tool to drive a care delivery and research agenda for integration of care and sharing of best practices across a health system. The Joint Commission Journal on Quality and Patient Safety, 43(1), pp.18-28.

[34] Görges, M., West, N.C., Christopher, N.A., Koch, J.L., Brodie, S.M., Lowlaavar, N., Lauder, G.R. and Ansermino, J.M., 2016. An ethnographic observational study to evaluate and optimize the use of respiratory acoustic monitoring in children receiving postoperative opioid infusions. Anesthesia & Analgesia, 122(4), pp.1132-1140.

[35] Grezio, A., Babeyko, A., Baptista, M.A., Behrens, J., Costa, A., Davies, G., Geist, E.L., Glimsdal, S., González, F.I., Griffin, J. and Harbitz, C.B., 2017. Probabilistic tsunami hazard analysis: multiple sources and global applications. Reviews of Geophysics, 55(4), pp.1158-1198.

[36] Grote, G., 2015. Promoting safety by increasing uncertainty–Implications for risk management. Safety science, 71, pp.71-79.

[37] Guerin, T.F., 2017. Evaluating expected and comparing with observed risks on a large-scale solar photovoltaic construction project: A case for reducing the regulatory burden. Renewable and Sustainable Energy Reviews, 74, pp.333-348.

[38] Gultekin, P., 2015. An Integrated Process Model and Decision Support System for Energy Efficient Retrofits.

[39] Guo, B.H. and Yiu, T.W., 2016. Developing leading indicators to monitor the safety conditions of construction projects. Journal of management in engineering, 32(1), p.04015016.

[40] Hardin, D., Stephan, E.G., Wang, W., Corbin, C.D. and Widergren, S.E., 2015. Buildings interoperability landscape (No. PNNL-25124). Pacific Northwest National Lab.(PNNL), Richland, WA (United States).

[41] Horberry, T., Burgess-Limerick, R. and Steiner, L.J., 2016. Human factors for the design, operation, and maintenance of mining equipment. CRC Press.

[42] Jain, R.K., Qin, J. and Rajagopal, R., 2017. Data-driven planning of distributed energy resources amidst socio-technical complexities. Nature Energy, 2(8), pp.1-11.

[43] James, P. and Walters, D., 2016. Supply chains and the protection of vulnerable workers. In Vulnerable Workers (pp. 57-74). Routledge.

[44] Janssen, M., Van Der Voort, H. and van Veenstra, A.F., 2015. Failure of large transformation projects from the viewpoint of complex adaptive systems: Management principles for dealing with project dynamics. Information Systems Frontiers, 17(1), pp.15-29.

[45] Jean-Baptiste, H., 2017. Financial Institutions Forecasting and Sensitivity Analysis for Decision Support Systems.

[46] Kache, F. and Seuring, S., 2017. Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International journal of operations & production management, 37(1), pp.10-36.

[47] Kant, V., 2016. Cyber-physical systems as sociotechnical systems: a view towards human–technology interaction. Cyber-Physical Systems, 2(1-4), pp.75-109.

[48] Kelly, T., 2017. The Role of the Regulator in SMS. International Transport Forum Discussion Paper.

[49] Kerr, H., 2016. Organizational resilience. Quality, 55(7), p.40.

[50] Kontogiannis, T., Leva, M.C. and Balfe, N., 2017. Total safety management: principles, processes and methods. Safety science, 100, pp.128-142.

[51] Kumari, R., 2017. From Spreadsheets to Stories: Creating Actionable Insights with Tableau and the Business Intelligence Lifecycle.

[52] Lam, J.S.L. and Su, S., 2015. Disruption risks and mitigation strategies: an analysis of Asian ports. Maritime Policy & Management, 42(5), pp.415-435.

[53] Landon, P., Weaver, P. and Fitch, J.P., 2016. Tracking minor and near-miss events and sharing lessons learned as a way to prevent accidents. Applied Biosafety, 21(2), pp.61-65.

[54] Lappi, T. and Aaltonen, K., 2017. Project governance in public sector agile software projects. International Journal of Managing Projects in Business, 10(2), pp.263-294.

[55] Lawal, A., Otokiti, B.O., Gobile, S., Okesiji, A., Oyasiji, O. and Adept, L.P., 2017. Taxation Law Compliance and Corporate Governance: Utilizing Business Analytics to Develop Effective Legal Strategies for Risk Management and Regulatory Adherence. Journal of Legal and Business Studies, 5(1), pp.1-10.

[56] Leveson, N., 2015. A systems approach to risk management through leading safety indicators. Reliability engineering & system safety, 136, pp.17-34.

[57] Lu, Y., Morris, K.C. and Frechette, S., 2016. Current standards landscape for smart manufacturing systems. National Institute of Standards and Technology, NISTIR, 8107(3), pp.1-39.

[58] Marsh, J.A. and Farrell, C.C., 2015. How leaders can support teachers with data-driven decision making: A framework for understanding capacity building. Educational Management Administration & Leadership, 43(2), pp.269-289.

[59] Maurino, D.E., Reason, J., Johnston, N. and Lee, R.B., 2017. Beyond aviation human factors: Safety in high technology systems. Routledge.

[60] McDonald, N., 2017. Organisational resilience and industrial risk. In Resilience engineering (pp. 155-180). CRC Press.

[61] McDonald, N., Callari, T.C., Stroeve, S., Baranzini, D., Woltjer, R. and Johansson, B.J.E., 2016. Safety mindfulness methodology. Future Sky Safety.

[62] Mullins, C., Fishell, G. and Tsien, R.W., 2016. Unifying views of autism spectrum disorders: a consideration of autoregulatory feedback loops. Neuron, 89(6), pp.1131-1156.

[63] Naweed, A. and Rainbird, S., 2015. Recovering time or chasing rainbows? Exploring time perception, conceptualization of time recovery, and time pressure mitigation in train driving. IIE Transactions on Occupational Ergonomics and Human Factors, 3(2), pp.91-104.

[64] Niesen, T., Houy, C., Fettke, P. and Loos, P., 2016, January. Towards an integrative big data analysis framework for data-driven risk management in industry 4.0. In 2016 49th Hawaii international conference on system sciences (HICSS) (pp. 5065-5074). IEEE.

[65] Niu, G., 2017. Data-driven technology for engineering systems health management. Springer Singapore, 10, pp.978-981.

[66] Nosek, B.A., Spies, J.R., Benjamin, M., Meyers, N., Abdelhamid, S., Aycock, D., Bonino, L.O., Bowman, S., Brown, C.T., Errington, T.M. and Geiger, B.J., 2017. An Open, FAIRified Data Commons: Proposal for NIH Data Commons Pilot.

[67] Nygren, M., Jakobsson, M., Andersson, E. and Johansson, B., 2017. Safety and multi-employer worksites in high-risk industries: An overview. Relations industrielles, 72(2), pp.223-245.

[68] Palermo, T., Power, M. and Ashby, S., 2017. Navigating institutional complexity: The production of risk culture in the financial sector. Journal of Management Studies, 54(2), pp.154-181.

[69] Portante, E.C., Kavicky, J.A., Craig, B.A., Talaber, L.E. and Folga, S.M., 2017. Modeling electric power and natural gas system interdependencies. Journal of Infrastructure Systems, 23(4), p.04017035.

[70] Preston, B.L., Backhaus, S.N., Ewers, M., Phillips, J.A., Silva-Monroy, C.A., Dagle, J.E., Tarditi, A.G., Looney, J. and King, T.J., 2016. Resilience of the US electricity system: A multi-hazard perspective. US Department of Energy Office of Policy. Washington, DC.

[71] Qian, Q. and Lin, P., 2016. Safety risk management of underground engineering in China: Progress, challenges and strategies. Journal of Rock Mechanics and Geotechnical Engineering, 8(4), pp.423-442.

[72] Qian, Q. and Lin, P., 2016. Safety risk management of underground engineering in China: Progress, challenges and strategies. Journal of Rock Mechanics and Geotechnical Engineering, 8(4), pp.42

[73] Read, R.R. and Rizkalla, M., 2015, July. Bridging the gap between qualitative, semi-quantitative and quantitative risk assessment of pipeline geohazards: the role of engineering judgment. In ASME International Pipeline Geotechnical Conference (Vol. 56918, p. V001T02A004). American Society of Mechanical Engineers.

[74] Rowlinson, S. and Jia, Y.A., 2015. Construction accident causality: an institutional analysis of heat illness incidents on site. Safety science, 78, pp.179-189.

[75] Sabatino, S. and Frangopol, D.M., 2017. Decision support system for optimum lifetime sustainability-based maintenance planning of highway bridges. Sustainable Infrastructure 2017, p.65.

[76] Salkin, C., Oner, M., Ustundag, A. and Cevikcan, E., 2017. A conceptual framework for Industry 4.0. In Industry 4.0: managing the digital transformation (pp. 3-23). Cham: Springer International Publishing.

[77] Sanders, C.B. and Sheptycki, J., 2017. Policing, crime and ‘big data’; towards a critique of the moral economy of stochastic governance. Crime, law and social change, 68(1), pp.1-15.

[78] Saunders, F.C., Gale, A.W. and Sherry, A.H., 2016. Responding to project uncertainty: Evidence for high reliability practices in large-scale safety–critical projects. International Journal of Project Management, 34(7), pp.1252-1265.

[79] Seshan, A. and Gorain, B.K., 2016. An integrated mining and metallurgical enterprise enabling continuous process optimization. In Innovative Process Development in Metallurgical Industry: Concept to Commission (pp. 203-242). Cham: Springer International Publishing.

[80] Shahtaheri, M., Haas, C.T. and Rashedi, R., 2017. Applying very large scale integration reliability theory for understanding the impacts of type II risks on megaprojects. Journal of Management in Engineering, 33(4), p.04017003.

[81] Sikula, N.R., Mancillas, J.W., Linkov, I. and McDonagh, J.A., 2015. Risk management is not enough: a conceptual model for resilience and adaptation-based vulnerability assessments. Environment Systems and Decisions, 35(2), pp.219-228.

[82] Simon, W.H., 2015. The organizational premises of administrative law. Law & Contemp. Probs., 78, p.61.

[83] Smith, B.T., Welch, T., Witt, A.M., Stewart, K.M., Lee, K., DeNeale, S.T., Bevelhimer, M.S., Burress, T.A., Pracheil, B.M., Pries, J.L. and O'Connor, P.W., 2017. A multi-year plan for research, development, and prototype testing of standard modular hydropower technology (No. ORNL/TM-2016/102). Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States). Center for Defect Physics (CDP).

[84] Sutcliffe, K.M., Paine, L. and Pronovost, P.J., 2017. Re-examining high reliability: actively organising for safety. BMJ quality & safety, 26(3), pp.248-251.

[85] Tan, S., De, D., Song, W.Z., Yang, J. and Das, S.K., 2016. Survey of security advances in smart grid: A data driven approach. IEEE Communications Surveys & Tutorials, 19(1), pp.397-422.

[86] Teece, D., Peteraf, M. and Leih, S., 2016. Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California management review, 58(4), pp.13-35.

[87] Thekdi, S. and Aven, T., 2016. An enhanced data-analytic framework for integrating risk management and performance management. Reliability Engineering & System Safety, 156, pp.277-287.

[88] Thusoo, A. and Sarma, J., 2017. Creating a data-driven enterprise with DataOps. O'Reilly Media, Incorporated.

[89] Tiusanen, R., 2017. Qualitative risk analysis. Handbook of Safety Principles, pp.463-492.

[90] Vankayala, S.C., 2016. Designing Data-Driven Automation Frameworks for Enterprise Systems: A Scalable Architecture for Continuous Intelligence. European Journal of Advances in Engineering and Technology, 3(12), pp.70-82.

[91] Vankayala, S.C., 2017. Embedding Quality Intelligence in API-First Architectures: Assurance Frameworks for Real-Time Financial Transactions. Journal of Scientific and Engineering Research, 4(6), pp.227-241.

[92] Verbeke, W., Baesens, B. and Bravo, C., 2017. Profit driven business analytics: a practitioner's guide to transforming big data into added value. John Wiley & Sons.

[93] Walter, I., 2016. The infrastructure finance challenge (p. 134). Open Book Publishers.

[94] Webb, C., Gao, L. and Song, L.G., 2016. Schedule compression impact on construction project safety. Frontiers of Engineering Management, 2(4), pp.344-350.

[95] Wehbe, F., Al Hattab, M. and Hamzeh, F., 2016. Exploring associations between resilience and construction safety performance in safety networks. Safety science, 82, pp.338-351.

[96] Weick, K.E. and Sutcliffe, K.M., 2015. Managing the unexpected: Sustained performance in a complex world. John Wiley & Sons.

[97] Wiering, M., Kaufmann, M., Mees, H., Schellenberger, T., Ganzevoort, W., Hegger, D.L., Larrue, C. and Matczak, P., 2017. Varieties of flood risk governance in Europe: How do countries respond to driving forces and what explains institutional change?. Global environmental change, 44, pp.15-26.

[98] Wu, P.P.Y., Fookes, C., Pitchforth, J. and Mengersen, K., 2015. A framework for model integration and holistic modelling of socio-technical systems. Decision Support Systems, 71, pp.14-27.

[99] Zeuschner, M. and Mo, J.P., 2015. Transformation pathway to high reliability support service system. Journal of Aerospace Operations, 3(3-4), pp.125-146.

[100] Zhou, K., Fu, C. and Yang, S., 2016. Big data driven smart energy management: From big data to big insights. Renewable and sustainable energy reviews, 56, pp.215-225.

@article{1714414,
      author = {Oghenepawon David Obriki, Oluwakemi Motunrayo Adegbuji},
      title = {Conceptual Modeling of Data-Driven Occupational Safety Risk Control in Large-Scale Energy Infrastructure Projects},
      journal = {Iconic Research And Engineering Journals},
      year = {2018},
      volume = {1},
      number = {7},
      pages = {169-189},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1714414.pdf},
      abstract = {Large-scale energy infrastructure projects such as oil and gas developments, power generation facilities, and transmission networks are characterized by high hazard intensity, complex socio-technical interactions, and multi-contractor delivery environments. These conditions create persistent challenges for effective occupational safety risk control, particularly when traditional safety management approaches remain reactive, compliance-driven, and reliant on lagging indicators. In response, this paper develops a conceptual model for data-driven occupational safety risk control tailored to the operational and governance realities of large-scale energy projects. The proposed model conceptualizes safety risk as a dynamic and measurable system property that can be proactively managed through the systematic integration of heterogeneous data sources. These include operational process data, workforce and competency records, incident and near-miss reports, environmental conditions, and real-time monitoring technologies. The model is structured around four interrelated layers: (1) risk sensing and data acquisition, (2) analytics and risk interpretation, (3) decision support and governance integration, and (4) risk control and intervention mechanisms. Emphasis is placed on the use of leading indicators, predictive analytics, and feedback loops to enable early identification of emerging hazards and weak signals across project phases. By explicitly linking data-driven insights to decision authority, escalation pathways, and safety governance structures, the model addresses a critical gap between analytical capability and practical risk control. It also highlights the importance of organizational learning, continuous model recalibration, and cross-contractor data integration in complex project ecosystems. The conceptual contribution of this work lies in framing occupational safety management not merely as an operational function, but as an adaptive, intelligence-enabled control system embedded within project governance. The model provides a foundation for future empirical research and offers practical guidance for project owners, EPC contractors, and regulators seeking to enhance safety performance, improve risk predictability, and strengthen resilience in large-scale energy infrastructure delivery.},
      keywords = {Occupational Safety, Data-Driven Risk Management, Energy Infrastructure Projects, Safety Analytics, Leading Indicators, Safety Governance, Large-Scale Projects},
      month = {January},
      doi = {https://doi.org/10.64388/IREV1I7-1714414}
  }