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Dynamic quantitative assessment of service resilience for long-distance energy pipelines under corrosion

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  • Huang, Yunfei
  • Qin, Guojin
  • Yang, Ming
  • Nogal, Maria

Abstract

Corrosion is a deterioration phenomenon of buried long-distance pipelines involving complex dynamic processes. The complexity poses challenges to addressing the safety concerns caused by corrosion. In recent years, the concept of resilience has been introduced into the assessment of engineering systems. However, there is a limited effort in quantitatively assessing the resilience of a pipeline's response to corrosion. This work aims to develop a novel framework to quantify the resilience of pipelines against corrosion while considering the resilience evolution induced by future corrosion growth, dynamic in-line inspection (ILI) plans, and distinct repair strategies (re-coating, composite material reinforcements, and pipe replacement). Pipeline Service Resilience (PSR) is modeled as a function of absorption, adaptability, and restoration capabilities based on the time-dependent burst pressure metric. Dynamic Monte Carlo Simulation technique is employed to model the potential resilience evolution scenarios to predict the PSR. The proposed framework is demonstrated on an in-service pipeline. The case results show that the PSR value ranges from 0.8943 to 1 due to the uncertainty of the resilience evolution process. Noteworthy impacts on PSR include repair time, ILI intervals, anti-corrosion ability, decision-making time, corrosion depth growth rate, and corrosion length growth rate (in decreasing order of sensitivity). The proposed methodology can potentially emerge as a significant tool for evaluating pipeline resilience under corrosion.

Suggested Citation

  • Huang, Yunfei & Qin, Guojin & Yang, Ming & Nogal, Maria, 2025. "Dynamic quantitative assessment of service resilience for long-distance energy pipelines under corrosion," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:reensy:v:256:y:2025:i:c:s0951832024008639
    DOI: 10.1016/j.ress.2024.110792
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    2. Zhong, Dan & Huang, Chaoyuan & Ma, Wencheng & Deng, Liming & Zhou, Jinbo & Xia, Ying, 2025. "Enhanced prediction of pipe failure through transient simulation-aided logistic regression," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    3. Qin, Guojin & Zhang, Chao & Wang, Bohong & Ni, Pingan & Wang, Yihuan, 2025. "An interpretable machine learning model for failure pressure prediction of blended hydrogen natural gas pipelines containing a crack-in-dent defect," Energy, Elsevier, vol. 320(C).

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