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.
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References
Bush CA, Uber JG (1998) Sampling design methods for water distribution model calibration. J Water Resour Plan Manag 124(6):334–344
Chaudhry, M.H. (2014) Applied hydraulic transients
Colombo AF, Lee P, Karney BW (2009) A selective literature review of transient-based leak detection methods. J Hydro Environ Res 2(4):212–227
Covas D, Ramos H (2010) Case studies of leak detection and location in water pipe systems by inverse transient analysis. J Water Resour Plan Manag 136(2):248–257
Duan H-F (2018) Accuracy and sensitivity evaluation of TFR method for leak detection in multiple-pipeline water supply systems. Water Resour Manag 32(6):2147–2164
Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7:457–472
Gong J, Lambert M, Zecchin A, Simpson A, Arbon N, Kim Y (2016a) Field study on non-invasive and non-destructive condition assessment for asbestos cement pipelines by time-domain fluid transient analysis. Struct Health Monit 15(1):113–124
Gong J, Lambert MF, Zecchin AC, Simpson AR (2016b) Experimental verification of pipeline frequency response extraction and leak detection using the inverse repeat signal. J Hydraul Res 54(2):210–219
Gong J, Stephens ML, Arbon NS, Zecchin AC, Lambert MF, Simpson AR (2015) On-site non-invasive condition assessment for cement mortar–lined metallic pipelines by time-domain fluid transient analysis. Struct Health Monit 14(5):426–438
Gupta A, Kulat K (2018) A selective literature review on leak management techniques for water distribution system. Water Resour Manag 32(10):3247–3269
Haghighi A, Ramos HM (2012) Detection of leakage freshwater and friction factor calibration in drinking networks using central force optimization. Water Resour Manag 26(8):2347–2363
Hutton CJ, Kapelan Z, Vamvakeridou-Lyroudia L, Savić DA (2012) Dealing with uncertainty in water distribution system models: a framework for real-time modeling and data assimilation. J Water Resour Plan Manag 140(2):169–183
Kang D, Lansey K (2009) Real-time demand estimation and confidence limit analysis for water distribution systems. J Hydraul Eng 135(10):825–837
Kang D, Pasha M, Lansey K (2009) Approximate methods for uncertainty analysis of water distribution systems. Urban Water J 6(3):233–249
Kapelan ZS, Savic DA, Walters GA (2007) Calibration of water distribution hydraulic models using a Bayesian-type procedure. J Hydraul Eng 133(8):927–936
Kim S (2014) Inverse transient analysis for a branched pipeline system with leakage and blockage using impedance method. Procedia Engineering 89:1350–1357
Lansey K, El-Shorbagy W, Ahmed I, Araujo J, Haan C (2001) Calibration assessment and data collection for water distribution networks. J Hydraul Eng 127(4):270–279
Lee PJ, Lambert MF, Simpson AR, Vítkovský JP, Liggett J (2006) Experimental verification of the frequency response method for pipeline leak detection. J Hydraul Res 44(5):693–707
Liggett JA, Chen L-C (1994) Inverse transient analysis in pipe networks. J Hydraul Eng 120(8):934–955
Meniconi S, Brunone B, Ferrante M, Massari C (2011) Small amplitude sharp pressure waves to diagnose pipe systems. Water Resour Manag 25(1):79–96
Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087–1092
Stephens ML, Lambert MF, Simpson AR (2013) Determining the internal wall condition of a water pipeline in the field using an inverse transient. J Hydraul Eng 139(3):310–324
Ter Braak CJ (2006) A Markov chain Monte Carlo version of the genetic algorithm differential evolution: easy Bayesian computing for real parameter spaces. Stat Comput 16(3):239–249
Vítkovský JP, Lambert MF, Simpson AR, Liggett JA (2007) Experimental observation and analysis of inverse transients for pipeline leak detection. J Water Resour Plan Manag 133(6):519–530
Vitkovsky, J.P., Lambert, M.F., Simpson, A.R. and Wang, X.-J. (2001) An experimental verification of the inverse transient technique for leak detection, p. 373, institution of engineers, Australia
Vrugt JA (2016) Markov chain Monte Carlo simulation using the DREAM software package: theory, concepts, and MATLAB implementation. Environ Model Softw 75:273–316
Vrugt JA, Ter Braak C, Diks C, Robinson BA, Hyman JM, Higdon D (2009) Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. International Journal of Nonlinear Sciences and Numerical Simulation 10(3):273–290
Wylie EB, Streeter VL, Suo L (1993) Fluid transients in systems. Prentice Hall Englewood Cliffs, NJ
Xie X, Zhang H, Hou D (2017) Bayesian approach for joint estimation of demand and roughness in water distribution systems. J Water Resour Plan Manag 143(8):04017034
Xu, X. and Karney, B. (2017) Modeling and monitoring of pipelines and networks, pp. 13-37, springer
Zeng W, Gong J, Cazzolato BS, Zecchin AC, Lambert MF, Simpson AR (2019) Condition assessment of pipelines using a bi-directional layer-peeling method and a dual-sensor configuration. J Sound Vib 457:181–196
Zhang C, Gong J, Lambert MF, Simpson AR, Zecchin AC (2019) Sensor placement strategy for pipeline condition assessment using inverse transient analysis. Water Resour Manag 33(8):2761–2774
Zhang C, Zecchin AC, Lambert MF, Gong J, Simpson AR (2018) Multi-stage parameter-constraining inverse transient analysis for pipeline condition assessment. J Hydroinf 20(2):281–300
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The research presented in this paper has been supported by the Australian Research Council through Discovery Project (DP170103715).
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Zhang, C., Lambert, M.F., Gong, J. et al. Bayesian Inverse Transient Analysis for Pipeline Condition Assessment: Parameter Estimation and Uncertainty Quantification. Water Resour Manage 34, 2807–2820 (2020). https://doi.org/10.1007/s11269-020-02582-9
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DOI: https://doi.org/10.1007/s11269-020-02582-9