| description abstract | Although the natural gas pipeline network is the most efficient and secure transportation mode for natural gas, it remains susceptible to external and internal risk factors. It is vital to address the associated risk factors such as corrosion, third-party interference, natural disasters, and equipment faults, which may lead to pipeline leakage or failure. The conventional quantitative risk assessment techniques require massive historical failure data that are sometimes unavailable or vague. Experts or researchers in the same field can always provide insights into the latest failure assessment picture. In this paper, fuzzy set theory is employed by obtaining expert elicitation through linguistic variables to obtain the failure probability of the top event (pipeline failure). By applying a combination of T- and S-Norms, the fuzzy aggregation approach can enable the most conservative risk failure assessment. The findings from this study showed that internal factors, including material faults and operational errors, significantly impact the pipeline failure integrity. Future directions should include sensitivity analyses to address the uncertainty in data to ensure the reliability of assessment results. Natural gas pipelines are efficient and reliable transportation modes. The integrity of these valuable assets is threatened by various risks such as corrosion, environmental factors, human errors, and mechanical faults. For newly developed or less monitored pipeline networks, historical data are either unavailable or faulty. To overcome this shortcoming, experts from pipeline networks can provide invaluable insight by providing their expert opinion. This study uses the expert’s elicitation by applying a fuzzy aggregation approach to predict the pipeline failure probability. The finding of this study confirmed that material faults and operational errors are the most critical risk factors leading to pipeline failure. The results of this study can be used to develop effective mitigation strategies for pipeline networks to minimize future failures. | |
