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Towards Decision-Making Support for Complex Socio-Technical System Safety Assessment: A Hybrid Model Combining Fram and Dynamic Bayesian Networks Publisher



Delikhoon M1 ; Habibi E1 ; Zarei E2, 3 ; Valdez Banda OA4, 5 ; Faridan M6
Authors
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Authors Affiliations
  1. 1. Department of Occupational Health and Safety Engineering, Faculty of Health, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Department of Safety Science, College of Aviation, Embry-Riddle Aeronautical University, Prescott, 86301, AZ, United States
  3. 3. Robertson Safety Institute (RSI), Embry-Riddle Aeronautical University, Prescott, 86301, AZ, United States
  4. 4. Research Group on Safe and Efficient Marine and Ship Systems (Marine and Arctic Technology), Department of Mechanical Engineering, Aalto University, Aalto, FI-00076, Finland
  5. 5. Kotka Maritime Research Centre, Kotka, 48100, Finland
  6. 6. Environmental Health Research Center, Department of Occupational Health and Safety at Work Engineering, Lorestan University of Medical Sciences, Khorramabad, Iran

Source: Process Safety and Environmental Protection Published:2024


Abstract

Effectively managing system safety and resilience in critical infrastructures requires addressing emergent risks and critical resonances. This study introduces a quantitative model that merges Monte Carlo Simulation (MCS) of the Functional Resonance Analysis Method (FRAM) and Dynamic Bayesian Network (DBN) to assess technological, organizational, and human performance variability in complex social-technical systems. FRAM identifies system taxonomy and functional variability, while MCS pinpoints critical coupling, supplying prior probabilities for functional resonance. This information feeds into the DBN, facilitating the modeling of causal relationships and probabilistic inferences regarding risk uncertainties and system resonances. The model underwent rigorous testing and validation on a preheater cyclone system within the cement industry. This process involved the utilization of historical field data gathered from six prominent cement industries and input from thirty subject matter experts. The integrated approach deeply analyzes emerging risk indicators, unveiling interactions within organizational, human, and technical subsets, alongside performance variability. Furthermore, the model incorporates system learning parameters, aiding decision-making under uncertainty. These findings advance system safety and resilience management, offering insights for risk assessment and accident prevention across diverse scenarios in complex socio-technical systems. © 2024 The Institution of Chemical Engineers
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