- -

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

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

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

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Ramírez-Aguilar, Roberto Xavier es_ES
dc.contributor.author López Jiménez, Petra Amparo es_ES
dc.contributor.author Torres Toro, David es_ES
dc.contributor.author Cobacho Jordán, Ricardo es_ES
dc.date.accessioned 2020-04-08T05:58:57Z
dc.date.available 2020-04-08T05:58:57Z
dc.date.issued 2020-03-01 es_ES
dc.identifier.issn 2073-4441 es_ES
dc.identifier.uri http://hdl.handle.net/10251/140507
dc.description.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. es_ES
dc.description.sponsorship 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. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Water es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Pipes es_ES
dc.subject Service life assessment es_ES
dc.subject Failure forecasting es_ES
dc.subject Asset management es_ES
dc.subject Weibull es_ES
dc.subject Bath curve es_ES
dc.subject.classification MECANICA DE FLUIDOS es_ES
dc.subject.classification INGENIERIA HIDRAULICA es_ES
dc.title A Front-Line and Cost-Effective Model for the Assessment of Service Life of Network Pipes es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/w12030667 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient es_ES
dc.description.bibliographicCitation 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/w12030667 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/w12030667 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 23 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 12 es_ES
dc.description.issue 3 es_ES
dc.relation.pasarela S\404329 es_ES
dc.description.references Shamir, 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.x es_ES
dc.description.references Kleiner, 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.2010.571 es_ES
dc.description.references Christodoulou, 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/s11269-009-9441-2 es_ES
dc.description.references Kutyłowska, M. (2015). Neural network approach for failure rate prediction. Engineering Failure Analysis, 47, 41-48. doi:10.1016/j.engfailanal.2014.10.007 es_ES
dc.description.references Motiee, 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.2018.137 es_ES
dc.description.references Di 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.2017.124 es_ES
dc.description.references Kutyłowska, M. (2018). Forecasting failure rate of water pipes. Water Supply, 19(1), 264-273. doi:10.2166/ws.2018.078 es_ES
dc.description.references Le 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)00057-1 es_ES
dc.description.references Alvisi, 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:10.1080/10286600802224064 es_ES
dc.description.references Kimutai, 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.1949-1204.0000196 es_ES
dc.description.references Santos, 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.2016.1148178 es_ES
dc.description.references Debón, 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.2009.07.004 es_ES
dc.description.references Davis, 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.2008.035 es_ES
dc.description.references Punurai, 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.2017.21.2.1 es_ES
dc.description.references Yoo, 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/w6123864 es_ES
dc.description.references D’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/w10030277 es_ES
dc.description.references Rajani, 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)00032-2 es_ES
dc.description.references Kropp, I., & Baur, R. (2005). Integrated failure forecasting model for the strategic rehabilitation planning process. Water Supply, 5(2), 1-8. doi:10.2166/ws.2005.0015 es_ES
dc.description.references García-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.2015.08.003 es_ES
dc.description.references Lei, 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-jfs es_ES
dc.description.references Datsiou, 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.2018.02.002 es_ES
dc.description.references Package survival https://cran.r-project.org/web/packages/survival/survival.pdf es_ES
dc.description.references Christodoulou, S. E. (2010). Water Network Assessment and Reliability Analysis by Use of Survival Analysis. Water Resources Management, 25(4), 1229-1238. doi:10.1007/s11269-010-9679-8 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem