Multidimensional risk evaluation of natural gas pipelines based on a multicriteria decision model using visualization tools and statistical tests for global sensitivity analysis

C.P. Medeiros, M.H. Alencar and A.T. de Almeida

Reliability Engineering and System Safety, 2017, vol. 165, issue C, 268-276

Abstract: Multidimensional risk analysis in pipelines has been addressed in the literature in recent years which has led to a greater understanding of risk in the decision context. A risk assessment model based on different perspectives becomes attractive to decision-makers (DMs) who are responsible for the maintenance of pipelines, and can help to prioritize maintenance efforts, and therefore optimize the use of human financial and other resources. As to the transportation of gas by pipeline, efforts at risk analysis must consider the physical and operational characteristics of the product, failure modes and their consequences, based on each accidental scenario considered. Different parameters are collected and/or estimated in order to produce a recommendation for the DM. Therefore, this paper enhances previous suggestions for a multicriteria decision model that evaluates multidimensional risk by using visualization tools and statistical tests as part of global sensitivity analysis. Simulations are made considering patterns which provide the DM with information about the uncertainty of different groups of parameters for the model. Furthermore, the output of the disturbance can be checked based on Kendall's correlation coefficient. Finally an evaluation can be made graphically of the different rankings of sections, thereby making a more assertive recommendation to the DM.

Keywords: Risk analysis; Natural gas pipeline; Sensitivity analysis; Monte Carlo simulation; Kendall's correlation coefficient; Multicriteria decision model (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:165:y:2017:i:c:p:268-276

DOI: 10.1016/j.ress.2017.04.002

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