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Adaptive approach for estimation of pipeline corrosion defects via Bayesian inference

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  • Kim, Kyeongsu
  • Lee, Gunhak
  • Park, Keonhee
  • Park, Seongho
  • Lee, Won Bo

Abstract

A framework to construct a model that predicts the corrosion defect distribution using a small amount of observation data is proposed in this study. A time-dependent generalized extreme value distribution was employed to consider the changing corrosion growth rate with time, and model parameters were estimated via Bayesian inferences to develop a robust prediction model. The model parameters were updated when a new batch of inspection data was available; previous data were not directly used but they indirectly assisted parameter estimation in the form of a prior distribution. In addition, an artificial data point representing a larger defect depth was added to the inspection data to ensure a conservative estimation of the model parameters and higher reliability of the model. The model was verified under three different cases, and the results showed that the suggested parameter estimation allowed the prediction model to adapt to the changing defect depth distribution in all three tested cases: 1) inspection data are available without measurement errors, 2) inspection data are available with measurement errors, and 3) the properties of the underground environment are drastically changed.

Suggested Citation

  • Kim, Kyeongsu & Lee, Gunhak & Park, Keonhee & Park, Seongho & Lee, Won Bo, 2021. "Adaptive approach for estimation of pipeline corrosion defects via Bayesian inference," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021005081
    DOI: 10.1016/j.ress.2021.107998
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    References listed on IDEAS

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    1. Gomes, Wellison J.S. & Beck, André T. & Haukaas, Terje, 2013. "Optimal inspection planning for onshore pipelines subject to external corrosion," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 18-27.
    2. A. Valor & F. Caleyo & L. Alfonso & J. C. Velázquez & J. M. Hallen, 2013. "Markov Chain Models for the Stochastic Modeling of Pitting Corrosion," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-13, July.
    3. Hazra, Indranil & Pandey, Mahesh D. & Manzana, Noldainerick, 2020. "Approximate Bayesian computation (ABC) method for estimating parameters of the gamma process using noisy data," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
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    3. Na, Kyumin & Yoon, Heonjun & Kim, Jaedong & Kim, Sungjong & Youn, Byeng D., 2023. "PERL: Probabilistic energy-ratio-based localization for boiler tube leaks using descriptors of acoustic emission signals," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Li, Xinhong & Jia, Ruichao & Zhang, Renren & Yang, Shangyu & Chen, Guoming, 2022. "A KPCA-BRANN based data-driven approach to model corrosion degradation of subsea oil pipelines," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    5. Hassan, Shamsu & Wang, Jin & Kontovas, Christos & Bashir, Musa, 2022. "An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    6. Wang, Zeyu & Shafieezadeh, Abdollah, 2023. "Bayesian updating with adaptive, uncertainty-informed subset simulations: High-fidelity updating with multiple observations," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    7. Wang, Zeyu & Shafieezadeh, Abdollah & Xiao, Xiong & Wang, Xiaowei & Li, Quanwang, 2022. "Optimal monitoring location for tracking evolving risks to infrastructure systems: Theory and application to tunneling excavation risk," Reliability Engineering and System Safety, Elsevier, vol. 228(C).

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