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Strong exploration of a cast iron pipe failure model

Author

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  • Moglia, M.
  • Davis, P.
  • Burn, S.

Abstract

A physical probabilistic failure model for buried cast iron pipes is described, which is based on the fracture mechanics of the pipe failure process. Such a model is useful in the asset management of buried pipelines. The model is then applied within a Monte-Carlo simulation framework after adding stochasticity to input variables. Historical failure rates are calculated based on a database of 81,595 pipes and their recorded failures, and model parameters are chosen to provide the best fit between historical and predicted failure rates. This provides an estimated corrosion rate distribution, which agrees well with experimental results. The first model design was chosen in a deliberate simplistic fashion in order to allow for further strong exploration of model assumptions. Therefore, first runs of the initial model resulted in a poor quantitative and qualitative fit in regards to failure rates. However, by exploring natural additional assumptions such as relating to stochastic loads, a number of assumptions were chosen which improved the model to a stage where an acceptable fit was achieved. The model bridges the gap between micro- and macro-level, and this is the novelty in the approach. In this model, data can be used both from the macro-level in terms of failure rates, as well as from the micro-level such as in terms of corrosion rates.

Suggested Citation

  • Moglia, M. & Davis, P. & Burn, S., 2008. "Strong exploration of a cast iron pipe failure model," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 885-896.
  • Handle: RePEc:eee:reensy:v:93:y:2008:i:6:p:885-896
    DOI: 10.1016/j.ress.2007.03.033
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    Cited by:

    1. Cordes, J.A. & Thomas, J. & Wong, R.S. & Carlucci, D., 2009. "Reliability estimates for flawed mortar projectile bodies," Reliability Engineering and System Safety, Elsevier, vol. 94(12), pages 1887-1893.
    2. 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.

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