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In the last decade, Bayesian networks (BNs) have been widely used in engineering risk assessment due to the benefits that they provide over other methods. Among these, the most significant is the ability to model systems, causal factors, and their dependencies in a probabilistic manner. This capability has enabled the community to do causal reasoning through associations, which answers questions such as: "How does new evidence x about the occurrence of event X change my belief about the occurrence of event Y ?" Associative reasoning has helped risk analysts to identify relevant risk-contributing factors and perform scenario analysis by evidence propagation. However, engineering risk assessment has yet to explore other features of BNs, such as the ability to reason through interventions, which enables the BN model to support answering questions of the form "How does doing X = x change my belief about the occurrence of event Y ?" In this article, we propose to expand the scope of use of BN models in engineering risk assessment to support intervention reasoning. This will provide more robust risk-informed decision support by enabling the modeling of policies and actions before being implemented. To do this, we provide the formal mathematical background and tools to model interventions in BNs and propose a framework that enables its use in engineering risk assessment. This is demonstrated in an illustrative case study on third-party damage of natural gas pipelines, showing how BNs can be used to inform decision-makers about the effect that new actions/policies can have on a system.

Despite the oil industry's efforts in improving safety, it still presents a high rate of serious accidents, many involving human failure events (HFE), which can be identified, modelled, and quantified through human reliability analysis (HRA). The oil industry commonly analyzes process safety by focusing on technical barriers, and thus it could benefit from HRA. Phoenix methodology is an HRA method that uses a human response model and relates the crew failures modes (CFM) to performance influencing factors (PIFs). Based on Phoenix CFMs and PIFs, two refinery accidents, the BP Texas City (2005) and the Chevron Richmond (2012), are analyzed in this paper. The analysis consists of the construction of the accident timeline; identification of the HFEs and assigning them to appropriate CFMs; and, finally analysis of the PIFs. The analysis helped better understand how the operators responded to an abnormal condition of the process, and why they took the actions they did, investigating the contribution of human error to the accidents. The assessment of the role human error played in these accidents is a major contribution to the understanding of why they happened, and a key information to avoid the same happening again in the future. Moreover, the features and limitations of the application of Phoenix HRA, which was developed based mainly on nuclear power plant operations, to Oil Refinery operation scenarios, are discussed and evaluated. This article provides insights on value of investigating the potential impact of human error in the Petroleum Industry accidents.

The market share of Tietê–Paraná inland waterway (TPIW) in the transport matrix of the São Paulo state, Brazil, is currently only 0.6%, but it is expected to increase to 6% over the next 20 years. In this scenario, to identify and explore potential undesired events a risk assessment is necessary. Part of this involves assigning the probability of occurrence of events, which usually is accomplished by a frequentist approach. However, in many cases, this approach is not possible due to unavailable or nonrepresentative data. This is the case of the TPIW that even though an expressive accident history is available, a frequentist approach is not suitable due to differences between current operational conditions and those met in the past. Therefore, a subjective assessment is an option as allows for working independently of the historical data, thus delivering more reliable results. In this context, this article proposes a methodology for assessing the probability of occurrence of undesired events based on expert opinion combined with fuzzy analysis. This methodology defines a criterion to weighting the experts and, using the fuzzy logic, evaluates the similarities among the experts’ beliefs to be used in the aggregation process before the defuzzification that quantifies the probability of occurrence of the events based on the experts’ opinion. Moreover, the proposed methodology is applied to the real case of the TPIW and the results obtained from the elicited experts are compared with a frequentist approach evidencing the impact on the results when considering different interpretations of the probability.

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