A Front-Line and Cost-Effective Model for the Assessment of Service Life of Network Pipes

Abstract

[EN] In any water utility, a reliable assessment of the service life of the network pipes is a key piece within the big puzzle of assets management. This paper presents a new statistical model (basic pipes life assessment, BPLA) to assess the service life of pipes, to locate the pipes on the failures bath curve and to forecast the expected failures in future years. Its main novelties are the processing of pipe information (is that information what is adapted to the classical maintenance engineering and not the other way back) and the definition of two different time variables that can be analyzed in parallel. The first novelty makes the model less demanding in terms of data and software tools than others currently available, and the second one allows to get all the results after one single stage of calculation. To show its usability, the BPLA has been applied to a pipe network that supplies water to 500,000 citizens for which two years of failure records are available. Procedures and results have been compared to the well-known Weibull proportional hazard model (WPHM), with final relative errors lower than 10% and 15% on each particular result.The authors would like to thank Global Omnium for the support provided, both directly and through the Catedra Aguas de Valencia of the UPV, for the development of the works presented in this paper.Ramírez-Aguilar, RX.; López Jiménez, PA.; Torres Toro, D.; Cobacho Jordán, R. (2020). A Front-Line and Cost-Effective Model for the Assessment of Service Life of Network Pipes. Water. 12(3):1-23. https://doi.org/10.3390/w12030667S123123Shamir, U., & Howard, C. D. D. (1979). An Analytic Approach to Scheduling Pipe Replacement. Journal - American Water Works Association, 71(5), 248-258. doi:10.1002/j.1551-8833.1979.tb04345.xKleiner, Y., Nafi, A., & Rajani, B. (2010). Planning renewal of water mains while considering deterioration, economies of scale and adjacent infrastructure. Water Supply, 10(6), 897-906. doi:10.2166/ws.2010.571Christodoulou, S., & Deligianni, A. (2009). A Neurofuzzy Decision Framework for the Management of Water Distribution Networks. Water Resources Management, 24(1), 139-156. doi:10.1007/s11269-009-9441-2Kutyłowska, M. (2015). Neural network approach for failure rate prediction. Engineering Failure Analysis, 47, 41-48. doi:10.1016/j.engfailanal.2014.10.007Motiee, H., & Ghasemnejad, S. (2018). Prediction of pipe failure rate in Tehran water distribution networks by applying regression models. Water Supply, 19(3), 695-702. doi:10.2166/ws.2018.137Di Nardo, A., Di Natale, M., Giudicianni, C., Greco, R., & Santonastaso, G. F. (2017). Complex network and fractal theory for the assessment of water distribution network resilience to pipe failures. Water Supply, 18(3), 767-777. doi:10.2166/ws.2017.124Kutyłowska, M. (2018). Forecasting failure rate of water pipes. Water Supply, 19(1), 264-273. doi:10.2166/ws.2018.078Le Gat, Y., & Eisenbeis, P. (2000). Using maintenance records to forecast failures in water networks. Urban Water, 2(3), 173-181. doi:10.1016/s1462-0758(00)00057-1Alvisi, S., & Franchini, M. (2010). Comparative analysis of two probabilistic pipe breakage models applied to a real water distribution system. Civil Engineering and Environmental Systems, 27(1), 1-22. doi:10.1080/10286600802224064Kimutai, E., Betrie, G., Brander, R., Sadiq, R., & Tesfamariam, S. (2015). Comparison of Statistical Models for Predicting Pipe Failures: Illustrative Example with the City of Calgary Water Main Failure. Journal of Pipeline Systems Engineering and Practice, 6(4), 04015005. doi:10.1061/(asce)ps.1949-1204.0000196Santos, P., Amado, C., Coelho, S. T., & Leitão, J. P. (2016). Stochastic data mining tools for pipe blockage failure prediction. Urban Water Journal, 14(4), 343-353. doi:10.1080/1573062x.2016.1148178Debón, A., Carrión, A., Cabrera, E., & Solano, H. (2010). Comparing risk of failure models in water supply networks using ROC curves. Reliability Engineering & System Safety, 95(1), 43-48. doi:10.1016/j.ress.2009.07.004Davis, P., Silva, D. D., Marlow, D., Moglia, M., Gould, S., & Burn, S. (2008). Failure prediction and optimal scheduling of replacements in asbestos cement water pipes. Journal of Water Supply: Research and Technology-Aqua, 57(4), 239-252. doi:10.2166/aqua.2008.035Punurai, W., & Davis, P. (2017). Prediction of Asbestos Cement Water Pipe Aging and Pipe Prioritization Using Monte Carlo Simulation. Engineering Journal, 21(2), 1-13. doi:10.4186/ej.2017.21.2.1Yoo, D., Kang, D., Jun, H., & Kim, J. (2014). Rehabilitation Priority Determination of Water Pipes Based on Hydraulic Importance. Water, 6(12), 3864-3887. doi:10.3390/w6123864D’Ercole, M., Righetti, M., Raspati, G., Bertola, P., & Maria Ugarelli, R. (2018). Rehabilitation Planning of Water Distribution Network through a Reliability—Based Risk Assessment. Water, 10(3), 277. doi:10.3390/w10030277Rajani, B., & Kleiner, Y. (2001). Comprehensive review of structural deterioration of water mains: physically based models. Urban Water, 3(3), 151-164. doi:10.1016/s1462-0758(01)00032-2Kropp, I., & Baur, R. (2005). Integrated failure forecasting model for the strategic rehabilitation planning process. Water Supply, 5(2), 1-8. doi:10.2166/ws.2005.0015García-Mora, B., Debón, A., Santamaría, C., & Carrión, A. (2015). Modelling the failure risk for water supply networks with interval-censored data. Reliability Engineering & System Safety, 144, 311-318. doi:10.1016/j.ress.2015.08.003Lei, Y. (2008). Evaluation of three methods for estimating the Weibull distribution parameters of Chinese pine (Pinus tabulaeformis ). Journal of Forest Science, 54(No. 12), 566-571. doi:10.17221/68/2008-jfsDatsiou, K. C., & Overend, M. (2018). Weibull parameter estimation and goodness-of-fit for glass strength data. Structural Safety, 73, 29-41. doi:10.1016/j.strusafe.2018.02.002Package survival https://cran.r-project.org/web/packages/survival/survival.pdfChristodoulou, S. E. (2010). Water Network Assessment and Reliability Analysis by Use of Survival Analysis. Water Resources Management, 25(4), 1229-1238. doi:10.1007/s11269-010-9679-

    Similar works

    Full text

    thumbnail-image

    Available Versions