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      Improved pool fire-initiated domino effect assessment in atmospheric tank farms using structural response

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      Reliability Engineering & System Safety
      Elsevier BV

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          Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures

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            The assessment of risk caused by domino effect in quantitative area risk analysis.

            A systematic procedure for the quantitative assessment of the risk caused by domino effect was developed. Escalation vectors, defined as the physical effects responsible of possible accident propagation, were identified for the primary scenarios usually considered in the QRA procedure. Starting from the assessment of the escalation vectors, the methodology allows the identification of credible domino scenarios and the estimation of their expected severity. A simplified technique was introduced for consequence and vulnerability assessment of domino scenarios. The overall contribution of domino effect to individual risk, societal risk and to the potential life loss index was calculated by a specific procedure, taking into account all the credible combinations of secondary events that may be triggered by each primary scenario. The development of a software package allowed the application of the procedure to several case-studies. The results evidenced the relevant modifications of the risk indexes caused by domino effect and the importance of including the quantitative analysis of domino effect in QRA, in order to correctly assess and control the risk caused by escalation scenarios.
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              A bibliometric review of process safety and risk analysis

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                Author and article information

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                Journal
                Reliability Engineering & System Safety
                Reliability Engineering & System Safety
                Elsevier BV
                09518320
                February 2024
                February 2024
                : 242
                : 109751
                Article
                10.1016/j.ress.2023.109751
                bf5f1f8b-d386-46fc-be2d-0ff004b4de28
                © 2024

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://doi.org/10.15223/policy-017

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-012

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-004

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