Resolving Left Truncation Issues and Enabling Pipe-Specific Likelihood of Failure Models
Publication: Pipelines 2024
ABSTRACT
Utility companies manage extensive water distribution pipe networks in urban areas, where aging infrastructure presents challenges in efficiently prioritizing rehabilitation and replacement projects. To address this issue, accurately assessing the condition of each pipe segment is essential. While various approaches exist, questions remain regarding their ability to handle the left truncation problem, create pipe-specific condition models, and maintain model interpretability. This study introduces a method that employs the inverse of time between failures (TBF) to represent pipe deterioration conditions. The method utilizes parametric statistical models to construct idealized pipe condition curves and leverages machine learning to capture complex relationships among factors influencing pipe deterioration, ultimately generating unique, pipe-specific models. The results demonstrate that the TBF approach, in conjunction with parametric models, aligns well with domain knowledge-based physical mechanisms. Moreover, machine learning offers the flexibility to include covariates. These findings are expected to offer utility companies a structured, data-driven approach for prioritizing pipe rehabilitation and replacement initiatives, enhancing the efficiency and reliability of infrastructure management.
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REFERENCES
Andreou, S. A., D. H. Marks, and R. M. Clark. 1987a. “A new methodology for modelling break failure patterns in deteriorating water distribution systems: Theory.” Advances in Water Resources, 10 (1): 2–10. https://doi.org/10.1016/0309-1708(87)90002-9.
Andreou, S. A., D. H. Marks, and R. M. Clark. 1987b. “A new methodology for modelling break failure patterns in deteriorating water distribution systems: Applications.” Advances in Water Resources, 10 (1): 11–20. https://doi.org/10.1016/0309-1708(87)90003-0.
Angkasuwansiri, T., and S. K. Sinha. 2013. “COMPREHENSIVE LIST OF PARAMETERS AFFECTING WASTEWATER PIPE PERFORMANCE.” 13 (2).
Barton, N. A., T. S. Farewell, S. H. Hallett, and T. F. Acland. 2019. “Improving pipe failure predictions: Factors affecting pipe failure in drinking water networks.” Water Research, 164: 114926. https://doi.org/10.1016/j.watres.2019.114926.
Breiman, L. 2001. “Random Forests.” Machine Learning, 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
Cox, D. R. 1972. “Regression Models and Life-Tables.” Journal of the Royal Statistical Society. Series B (Methodological), 34 (2): 187–220. [Royal Statistical Society, Wiley].
Grinsztajn, L., E. Oyallon, and G. Varoquaux. 2022. “Why do tree-based models still outperform deep learning on tabular data?” arXiv.
Jenkins, L., S. Gokhale, and M. McDonald. 2015. “Comparison of Pipeline Failure Prediction Models for Water Distribution Networks with Uncertain and Limited Data.” Journal of Pipeline Systems Engineering and Practice, 6 (2): 04014012. American Society of Civil Engineers. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000181.
Kleiner, Y., and B. Rajani. 2001. “Comprehensive review of structural deterioration of water mains: statistical models.” Urban Water, Ground Water in the Environment, 3 (3): 131–150. https://doi.org/10.1016/S1462-0758(01)00033-4.
Kuhn, M. 2008. “Building predictive models in R using the caret package.” J Stat Softw, 28 (5): 1–26.
Liu, Y., Y. Wang, and J. Zhang. 2012. “New Machine Learning Algorithm: Random Forest.” Information Computing and Applications, Lecture Notes in Computer Science, B. Liu, M. Ma, and J. Chang, eds., 246–252. Berlin, Heidelberg: Springer.
Røstum, J. 2000. “STATISTICAL MODELLING OF PIPE FAILURES IN WATER NETWORKS.”
Scheidegger, A., J. P. Leitão, and L. Scholten. 2015. “Statistical failure models for water distribution pipes – A review from a unified perspective.” Water Research, 83: 237–247. https://doi.org/10.1016/j.watres.2015.06.027.
Snider, B., and E. A. McBean. 2020. “Improving Urban Water Security through Pipe-Break Prediction Models: Machine Learning or Survival Analysis.” Journal of Environmental Engineering, 146 (3): 04019129. American Society of Civil Engineers. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001657.
Thomson, J., S. Flamberg, and W. Condit. 2013. Primer on Condition Curves for Water Mains. United States Environmental Protection Agency.
Xu, H., and S. K. Sinha. 2021. “Modeling Pipe Break Data Using Survival Analysis with Machine Learning Imputation Methods.” Journal of Performance of Constructed Facilities, 35 (5): 04021071. American Society of Civil Engineers. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001649
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Published online: Aug 30, 2024
ASCE Technical Topics:
- Analysis (by type)
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Failure analysis
- Infrastructure
- Lifeline systems
- Mathematics
- Parameters (statistics)
- Pipe failures
- Pipe networks
- Pipeline management
- Pipeline systems
- Pipes
- Statistics
- Utilities
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