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Pipe break prediction based on evolutionary data-driven methods with brief recorded data

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Listed:
  • Xu, Qiang
  • Chen, Qiuwen
  • Li, Weifeng
  • Ma, Jinfeng

Abstract

Pipe breaks often occur in water distribution networks, imposing great pressure on utility managers to secure stable water supply. However, pipe breaks are hard to detect by the conventional method. It is therefore necessary to develop reliable and robust pipe break models to assess the pipe's probability to fail and then to optimize the pipe break detection scheme. In the absence of deterministic physical models for pipe break, data-driven techniques provide a promising approach to investigate the principles underlying pipe break. In this paper, two data-driven techniques, namely Genetic Programming (GP) and Evolutionary Polynomial Regression (EPR) are applied to develop pipe break models for the water distribution system of Beijing City. The comparison with the recorded pipe break data from 1987 to 2005 showed that the models have great capability to obtain reliable predictions. The models can be used to prioritize pipes for break inspection and then improve detection efficiency.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:8:p:942-948
    DOI: 10.1016/j.ress.2011.03.010
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Debón, A. & Carrión, A. & Cabrera, E. & Solano, H., 2010. "Comparing risk of failure models in water supply networks using ROC curves," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 43-48.
    4. Yamijala, Shridhar & Guikema, Seth D. & Brumbelow, Kelly, 2009. "Statistical models for the analysis of water distribution system pipe break data," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 282-293.
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    Cited by:

    1. Qiang Xu & Zhimin Qiang & Qiuwen Chen & Kuo Liu & Nan Cao, 2018. "A Superposed Model for the Pipe Failure Assessment of Water Distribution Networks and Uncertainty Analysis: A Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1713-1723, March.
    2. Jara-Arriagada, Carlos & Stoianov, Ivan, 2021. "Pipe breaks and estimating the impact of pressure control in water supply networks," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    3. Robles-Velasco, Alicia & Cortés, Pablo & Muñuzuri, Jesús & Onieva, Luis, 2020. "Prediction of pipe failures in water supply networks using logistic regression and support vector classification," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    4. Iman Moslehi & Mohammadreza Jalili_Ghazizadeh, 2020. "Pressure-Pipe Breaks Relationship in Water Distribution Networks: A Statistical Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2851-2868, July.

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