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A physical probabilistic model to predict failure rates in buried PVC pipelines

Author

Listed:
  • Davis, P.
  • Burn, S.
  • Moglia, M.
  • Gould, S.

Abstract

For older water pipeline materials such as cast iron and asbestos cement, future pipe failure rates can be extrapolated from large volumes of existing historical failure data held by water utilities. However, for newer pipeline materials such as polyvinyl chloride (PVC), only limited failure data exists and confident forecasts of future pipe failures cannot be made from historical data alone. To solve this problem, this paper presents a physical probabilistic model, which has been developed to estimate failure rates in buried PVC pipelines as they age. The model assumes that under in-service operating conditions, crack initiation can occur from inherent defects located in the pipe wall. Linear elastic fracture mechanics theory is used to predict the time to brittle fracture for pipes with internal defects subjected to combined internal pressure and soil deflection loading together with through-wall residual stress. To include uncertainty in the failure process, inherent defect size is treated as a stochastic variable, and modelled with an appropriate probability distribution. Microscopic examination of fracture surfaces from field failures in Australian PVC pipes suggests that the 2-parameter Weibull distribution can be applied. Monte Carlo simulation is then used to estimate lifetime probability distributions for pipes with internal defects, subjected to typical operating conditions. As with inherent defect size, the 2-parameter Weibull distribution is shown to be appropriate to model uncertainty in predicted pipe lifetime. The Weibull hazard function for pipe lifetime is then used to estimate the expected failure rate (per pipe length/per year) as a function of pipe age. To validate the model, predicted failure rates are compared to aggregated failure data from 17 UK water utilities obtained from the United Kingdom Water Industry Research (UKWIR) National Mains Failure Database. In the absence of actual operating pressure data in the UKWIR database, typical values from Australian water utilities were assumed to apply. While the physical probabilistic failure model shows good agreement with data recorded by UK water utilities, actual operating pressures from the UK is required to complete the model validation.

Suggested Citation

  • Davis, P. & Burn, S. & Moglia, M. & Gould, S., 2007. "A physical probabilistic model to predict failure rates in buried PVC pipelines," Reliability Engineering and System Safety, Elsevier, vol. 92(9), pages 1258-1266.
  • Handle: RePEc:eee:reensy:v:92:y:2007:i:9:p:1258-1266
    DOI: 10.1016/j.ress.2006.08.001
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    Citations

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    Cited by:

    1. Liu, Aihua & Chen, Ke & Huang, Xiaofei & Li, Didi & Zhang, Xiaochun, 2021. "Dynamic risk assessment model of buried gas pipelines based on system dynamics," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    2. Mohammad Taghi Tahooneh & Reza Dashti, 2022. "A new model for calculating rational failure rates based on network assets worth," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2221-2233, October.
    3. ValinÄ ius, Mindaugas & ŽutautaitÄ—, Inga & Dundulis, Gintautas & RimkeviÄ ius, Sigitas & Janulionis, Remigijus & Bakas, Rimantas, 2015. "Integrated assessment of failure probability of the district heating network," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 314-322.
    4. Xu, Qiang & Chen, Qiuwen & Li, Weifeng & Ma, Jinfeng, 2011. "Pipe break prediction based on evolutionary data-driven methods with brief recorded data," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 942-948.
    5. Jaber Alkasseh & Mohd Adlan & Ismail Abustan & Hamidi Aziz & Abu Hanif, 2013. "Applying Minimum Night Flow to Estimate Water Loss Using Statistical Modeling: A Case Study in Kinta Valley, Malaysia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1439-1455, March.
    6. Zhou, Zhi-Jie & Hu, Chang-Hua & Xu, Dong-Ling & Chen, Mao-Yin & Zhou, Dong-Hua, 2010. "A model for real-time failure prognosis based on hidden Markov model and belief rule base," European Journal of Operational Research, Elsevier, vol. 207(1), pages 269-283, November.

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