Bayesian Inverse Transient Analysis for Pipeline Condition Assessment: Parameter Estimation and Uncertainty Quantification

Chi Zhang (), Martin F. Lambert (), Jinzhe Gong (), Aaron C. Zecchin (), Angus R. Simpson () and Mark L. Stephens ()
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Chi Zhang: University of Adelaide
Martin F. Lambert: University of Adelaide
Jinzhe Gong: University of Adelaide
Aaron C. Zecchin: University of Adelaide
Angus R. Simpson: University of Adelaide
Mark L. Stephens: Asset Analytics Lead; Asset Management Department

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2020, vol. 34, issue 9, No 11, 2807-2820

Abstract: Abstract Strategic pipeline asset management requires accurate and up-to-date information on pipeline condition. As a tool for pipeline condition assessment, inverse transient analysis (ITA - a pipeline model calibration approach) is typically formulated as a deterministic problem, and optimization methods are used for searching a single best solution. The uncertainty associated with the single best solution is rarely assessed. In this paper, the pipeline model calibration problem is formulated as a Bayesian inverse problem, and a Markov Chain Monte Carlo (MCMC) based method is used to construct the estimated posterior probability density function (PDF) of the calibration parameters. The MCMC based method is able to achieve parameter estimation and uncertainty assessment in a single run, which is confirmed by numerical experiments. The proposed technique is also validated using measured hydraulic transient response data from an experimental laboratory pipeline system. Two thinner-walled pipe sections (simulating extended deterioration) are successfully identified with an assessment of the parameter uncertainty. The results also suggest that proper sensor placement can reduce parameter uncertainty and significantly enhance system identifiability.

Keywords: Markov chain Monte Carlo; Hydraulic transient; Inverse transient analysis; Uncertainty assessment; Pipeline condition assessment (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11269-020-02582-9

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