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Statistical models for the analysis of water distribution system pipe break data

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  • Yamijala, Shridhar
  • Guikema, Seth D.
  • Brumbelow, Kelly

Abstract

The deterioration of pipes leading to pipe breaks and leaks in urban water distribution systems is of concern to water utilities throughout the world. Pipe breaks and leaks may result in reduction in the water-carrying capacity of the pipes and contamination of water in the distribution systems. Water utilities incur large expenses in the replacement and rehabilitation of water mains, making it critical to evaluate the current and future condition of the system for maintenance decision-making. This paper compares different statistical regression models proposed in the literature for estimating the reliability of pipes in a water distribution system on the basis of short time histories. The goals of these models are to estimate the likelihood of pipe breaks in the future and determine the parameters that most affect the likelihood of pipe breaks. The data set used for the analysis comes from a major US city, and these data include approximately 85,000 pipe segments with nearly 2500 breaks from 2000 through 2005. The results show that the set of statistical models previously proposed for this problem do not provide good estimates with the test data set. However, logistic generalized linear models do provide good estimates of pipe reliability and can be useful for water utilities in planning pipe inspection and maintenance.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:2:p:282-293
    DOI: 10.1016/j.ress.2008.03.011
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    Cited by:

    1. 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.
    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. 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. Kabir, Golam & Tesfamariam, Solomon & Sadiq, Rehan, 2015. "Predicting water main failures using Bayesian model averaging and survival modelling approach," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 498-514.
    5. Chen, Thomas Ying-Jeh & Guikema, Seth David & Daly, Craig Michael, 2019. "Optimal pipe inspection paths considering inspection tool limitations," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 156-166.
    6. Liu, Wei & Song, Zhaoyang & Ouyang, Min, 2020. "Lifecycle operational resilience assessment of urban water distribution networks," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    7. 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).
    8. Rifaai, Talha M. & Abokifa, Ahmed A. & Sela, Lina, 2022. "Integrated approach for pipe failure prediction and condition scoring in water infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    9. Feng, Liuyang & Zhang, Limao, 2021. "Assessment of tunnel face stability subjected to an adjacent tunnel," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    10. Meireles, Inês & Sousa, Vitor & Matos, José Pedro & Cruz, Carlos Oliveira, 2023. "Determinants of water loss in Portuguese utilities," Utilities Policy, Elsevier, vol. 83(C).
    11. Francis, Royce A. & Guikema, Seth D. & Henneman, Lucas, 2014. "Bayesian Belief Networks for predicting drinking water distribution system pipe breaks," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 1-11.
    12. Kiyeon Kim & Joonyoung Kim & Tae-Young Kwak & Choong-Ki Chung, 2018. "Logistic regression model for sinkhole susceptibility due to damaged sewer pipes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(2), pages 765-785, September.
    13. Rahimi-Golkhandan, Armin & Aslani, Babak & Mohebbi, Shima, 2022. "Predictive resilience of interdependent water and transportation infrastructures: A sociotechnical approach," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    14. Daulat, Shamsuddin & Rokstad, Marius Møller & Bruaset, Stian & Langeveld, Jeroen & Tscheikner-Gratl, Franz, 2024. "Evaluating the generalizability and transferability of water distribution deterioration models," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    15. Kozłowski Edward & Kowalska Beata & Kowalski Dariusz & Mazurkiewicz Dariusz, 2019. "Survival Function in the Analysis of the Factors Influencing the Reliability of Water Wells Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4909-4921, November.
    16. Alicia Robles-Velasco & Pablo Cortés & Jesús Muñuzuri & Luis Onieva, 2021. "Estimation of a logistic regression model by a genetic algorithm to predict pipe failures in sewer networks," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(3), pages 759-776, September.
    17. Fan, Xudong & Wang, Xiaowei & Zhang, Xijin & ASCE Xiong (Bill) Yu, P.E.F., 2022. "Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors," Reliability Engineering and System Safety, Elsevier, vol. 219(C).

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