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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.
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This content will become publicly available on May 1, 2025
Optimizing natural gas pipeline risk assessment using hybrid fuzzy bayesian networks and expert elicitation for effective decision-making strategies
Natural gas pipelines are susceptible to external and internal risk factors, such as corrosion, environmental conditions, external interferences, construction and design faults, and equipment failures. Bayesian Networks (BN) is a promising risk assessment approach widely used to evaluate these risk factors. One of BN's inherent limitations is its inability to accurately capture statistical dependencies and causal relationships, which can be overcome by incorporating expert elicitation into BN. To account for uncertainty and vagueness in assessing pipeline failure risks, fuzzy set theory (FST) can be combined with BN, commonly known as Fuzzy Bayesian Networks (FBN). This study developed an FBN framework that uses linguistic variables to calculate fuzzy probability (FPr) through domain expert elicitation, and crisp probabilities (CPr) are computed using historical incident data from the Pipeline and Hazardous Materials Safety Administration (PHMSA). Based on the findings from the case study of the Midwest region of the USA, external factors, i.e., third-party interference, outside force, and other incidents, significantly impact pipeline performance and reliability. Diagnosis inference indicates that in the Midwest region of the USA, pipeline material and age are critical factors leading to corrosion failure by threatening pipeline integrity. The findings from this study suggested that a targeted risk mitigation strategy is paramount for minimizing the risks associated with pipeline networks.
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- Award ID(s):
- 1750316
- PAR ID:
- 10522975
- Publisher / Repository:
- ScienceDirect
- Date Published:
- Journal Name:
- Gas Science and Engineering
- Volume:
- 125
- Issue:
- C
- ISSN:
- 2949-9089
- Page Range / eLocation ID:
- 205283
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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