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Bayesian analysis of small probability incidents for corroding energy pipelines

Bayesian analysis of small probability incidents for corroding energy pipelines

Pesinis, Konstantinos and Tee, Kong Fah ORCID: 0000-0003-3202-873X (2018) Bayesian analysis of small probability incidents for corroding energy pipelines. Engineering Structures, 165. pp. 264-277. ISSN 0141-0296 (doi:https://doi.org/10.1016/j.engstruct.2018.03.038)

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Abstract

This paper presents a new methodology for estimation of small posterior failure probabilities for underground energy pipelines, based on external corrosion inspection data. The analysis of the data is based on the BUS (Bayesian Updating with Structural reliability methods) that sets an analogy between Bayesian updating and a reliability problem. The structural reliability method adopted herein is Subset Simulation (SuS) and the whole analysis is referred to as BUS-SuS. Corrosion data obtained from multiple in-line inspections (ILI) of an underground natural gas pipeline are used to illustrate and validate the proposed methodology. The growth of the corrosion defects is modelled through an hierarchical Bayesian framework and the ILI associated measurement errors are comprehensively considered. Through this efficient method, it is ensured that the final samples have reached the posterior distribution. It is also more advantageous over other methods typically employed for Bayesian analysis of corroding pipelines, because it allows the estimation of small posterior failure probabilities directly within the same framework. The proposed methodology can be incorporated in a reliability-based pipeline integrity management program to assist engineers in selecting suitable maintenance strategies.

Item Type: Article
Uncontrolled Keywords: Pipelines; Bayesian updating; Subset simulation; Structural reliability; In-line inspection
Subjects: T Technology > TD Environmental technology. Sanitary engineering
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Engineering Science
Last Modified: 15 May 2019 12:42
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: GREAT d
Selected for GREAT 2019: GREAT 6
URI: http://gala.gre.ac.uk/id/eprint/19740

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