Journal of Health and Safety at Work

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Introduction: Domino effects are a chain of low-probability and high-consequence accidents in which a primary event (fire or explosion) in one unit causes secondary events in adjacent units. Bayesian networks have been used to model the propagation patterns of domino effects and to estimate the probability of these effects at different levels. The unique modeling and flexible structure provided by Bayesian networks allow the analysis of domino effects through a probabilistic framework, taking synergistic effects into account.
Material and Methods: Firstly, collecting the basic information related to the location of the storage tanks and determining the scenario of the accidents were done. Furthermore, the values of the heat radiation as escalation vectors in case of a fire in one tank were determined using ALOHA software. The received heat flux values were compared with the heat radiation threshold of 15  kw/m² and the escalation probability of the primary unit and the propagation of the initial scenario to nearby storage tanks were determined using Bayesian networks.
Results: The analysis of the heat flux values showed that among the 8 studied storage tanks, two storage tanks had the highest potential for spreading domino effects due to their location in a tank farm. Also, the implementation of Bayesian networks in GeNIe revealed that, compared to other storage tanks, the probability of domino effects propagating to other nodes is higher when a primary fire accident occurs in the two mentioned tanks, while considered as primary units.
Conclusion: Domino effect modeling and appropriate preventative measures can decrease the escalation probability in the process industries. Consideration of the synergistic effects of events at different levels by taking the escalation vectors into account leads to proper risk management and the determination of emergency response measures in storage tank farms.

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Introduction: Safety in process industries is of paramount importance, as these industries typically deal with hazardous chemicals and complex processes that can lead to irreparable consequences in the event of accidents. The present study aims to evaluate domino effects and analyze the vulnerability of storage tanks using graph theory and Bayesian networks in a process industry. This approach can help identify system vulnerabilities and facilitate the prediction of potential accidents, ultimately leading to improved safety measures.
Material and Methods: In this study, after collecting initial information related to the location of storage tanks and determining accident scenarios, the tanks under investigation were selected based on the type of stored materials and their layout, with input from experts. These tanks were modeled as nodes in a graph, and the probability of accident spreading among them was represented as edges in the graph based on the amount of heat radiation. Additionally, for modeling domino effects and analyzing vulnerability, graph theory and Bayesian networks were employed.
Results: Based on the target tanks related to the pool fire scenario, domino effects in the tanks were identified and modeled as a theory graph. Tank number 4 was determined to be the most influential and susceptible tank in the spread and initiation of domino effects, with the highest betweenness index (0.2381), outcloseness index (0.35211), and incloseness index (0.3663). Additionally, based on the allcloseness index, the most likely sequence of the tank involvement in fires caused by domino effects was identified.
Conclusion: In order to reduce the likelihood of exacerbating domino effects, modeling the effects using Bayesian networks and graph theory is proposed; the results can also be applied to optimize fire suppression strategies. Additionally, vulnerability analysis through graph theory and the assessment of tanks regarding their potential for fire initiation and spread can be beneficial in managing the risks associated with domino effects.

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Introduction: Fire risk assessment in oil storage tanks faces challenges due to incomplete, conflicting, and uncertain data, particularly when empirical evidence is limited. Traditional point-based likelihood estimates often fail to capture expert doubt and epistemic uncertainty. This study aims to develop and evaluate a novel hybrid framework combining Dempster-Shafer Theory (DST) and Bayesian Networks (BN) to improve the trustworthiness of fire risk prediction in such industrial settings.
Material and Methods: The proposed approach integrates DST to model expert uncertainty through interval probabilities (Bel–Pl) and BN to dynamically update causal relationships as new information appears. The study implements computational coding to enable DST calculations for five expert opinions across 243 scenarios, overcoming prior limitations in multi-expert modeling due to computational complexity.
Results: The hybrid DST-BN framework demonstrated superior ability to incorporate incomplete and conflicting expert data, reducing overconfidence linked to point estimates. Interval probabilities offered more trustworthy representations of epistemic uncertainty, while BN integration allowed traceable and adaptable causal modeling. The computational solution facilitated practical application of DST with multiple experts, enhancing the strength of the risk assessment.
Conclusion: This research provides an effective DST-BN hybrid methodology for assessing fire risk in fixed-roof oil tanks, improving accuracy and trustworthiness in complex industrial environments. By addressing the shortcomings of point-based methods and enabling multi-expert participation, the framework supports clearer and more defensible probabilistic inferences. Future work may focus on integrating real-time sensor data and AI-based decision systems to further strengthen dynamic risk assessment capabilities.