As accidents were and still one of the main reasons standing behind the increasing rates of casualties such as death, injuries, and evacuations, the constant improvement of safety measures especially in the field of hydrocarbons remains a major concern. Therefore, in the work in hand, we attempt to shed the light on the ways of developing a method for the evaluation of risks of fire and explosions of pipelines. The causes of the latter and consequences are, in one hand, analyzed by means of fault tree and bow tie methods. On the other hand, a quantitative analysis implementing the Bayesian networks is used to estimate the probability of occurrence of the adverse event. Moreover, 72 basic events were found to be of the primary causes provoking the occurrence of undesirable events. However, some experts often find it difficult to precisely determine the probabilities of occurrence of basic events of the tree. For the purpose of evaluating the occurrence of each basic event, we used the fuzzy logic. Hence, at the end of the study, we were able to develop a model that could help us evaluate the risks accompanied the fires and pipelines explosion as well as the consequences.

Probabilistic modeling is widespread in engineering practices, mainly to evaluate the safety, risk analysis, and reliability of complex systems. However, insufficient data makes it difficult to estimate the state probability of components or the global system in dynamic complex systems. Furthermore, conventional methods for dependability analysis typically have little capacity to cope with dependence, failure behavior, epistemic uncertainty, and common cause failure simultaneously. This paper presents the application of an extended discrete-time dynamic evidential network (DEN) model to assess the availability of complex systems. The model application combines Dempster-Shafer's theory to treat epistemic uncertainty over a new state-space reconstruction of components and the dynamic Bayesian network to present multi-state system dependability. This model is demonstrated in a real case study of a water deluge system installed as a safety barrier from Algeria's oil and gas plant. The results show the significant influence of these factors on the system's availability. The goal of this modeling is to assure the high availability of a safety barrier in a volatile setting by providing a decision-making tool to prioritize maintenance tasks, preventing the failure of complicated redundant systems, and recommending alterations to the design.

The world is currently witnessing high rainfall variability at the spatiotemporal level. In this paper, data from three representative rain gauges in northern Algeria, from 1920 to 2011, at an annual scale, were used to assess a relatively new hybrid method, which combines the innovative triangular trend analysis (ITTA) with the orthogonal discrete wavelet transform (DWT) for partial trend identification. The analysis revealed that the period from 1950 to 1975 transported the wettest periods, followed by a long-term dry period beginning in 1973. The analysis also revealed a rainfall increase during the latter decade. The combined method (ITTA–DWT) showed a good efficiency for extreme rainfall event detection. In addition, the analysis indicated the inter- to multiannual phenomena that explained the short to medium processes that dominated the high rainfall variability, masking the partial trend components existing in the rainfall time series and making the identification of such trends a challenging task. The results indicate that the approaches—combining ITTA and selected input combination models resulting from the DWT—are auspicious compared to those found using the original rainfall observations. This analysis revealed that the ITTA–DWT method outperformed the ITTA method for partial trend identification, which proved DWT’s efficiency as a coupling method.

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The study of the effects of mixing potable water with wastewater is a complex and difficult research area. This difficulty is because water and sewage networks are subject to various physical, environmental, and operational factors. The main objective of the study was to propose a new comprehensive framework for analyzing and assessing water quality based on Bayesian networks. An intervention plan was proposed to reduce the consequences of water quality and networks failure. The proposed framework was applied to water distribution network of Mdaourouch city (Souk Ahras, Algeria) to demonstrate its effectiveness. The results indicated that the water contamination rate has reached 33.9 %, which caused severe consequences. The effectiveness of the proposed plan has been verified theoretically using simulations, and the results have proven to be very satisfactory. The proposed model is a decision support tool, which is expected to assist decision‐makers and engineers in reviewing their plans and making the right decision. Practitioner Points This paper proposes a novel comprehensive framework for analyzing and assessing water quality and failure consequences based on Bayesian networks. This paper revisits the failure consequences. An intervention plan is proposed to reduce failure consequences. Results demonstrate that the proposed plan leads to fewer consequences probabilities. The proposed method can give the probability of failure of water and sewer network.