Predicting water pipe breaks using neural network

Ahn, Jae-Chan; Lee, S.W.; Lee, G.S.; Koo, Ja−Yong

doi:10.2166/ws.2005.0096

Cited by 23 publications

(12 citation statements)

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“…Schuster and McBean (2008) and O'Day (1982) derived failure probability models based on the time of previous failures, soil types, pipe diameters, environmental conditions and age. Ahn et al (2005) observed that most types of pipes (ductile iron, steel, and asbestos cement) did not have an increased number of failures during the colder winter months but that cast iron did show a significant increase in the number of failures during the winter months.…”

Section: Precedent Models For Watermain Failurementioning

confidence: 93%

“…The models were trained with historical input data including temperature, rainfall, operating pressure, and number of breaks. Ahn et al (2005) used an ANN model for predicting water pipe breaks in service pipes and mains in Seoul (Korea); they observed good performance for a prediction model based on pipe characteristics and water and soil temperatures. Moselhi and Shehab-Eldeen (2000) employed an ANN in the analyses and classification of defects in sewer pipelines.…”

Section: Precedent Models For Watermain Failurementioning

confidence: 99%

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Forecasting watermain failure using artificial neural network modelling

Asnaashari

McBean

Gharabaghi

et al. 2013

Canadian Water Resources Journal

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After rapid urban expansion in Ontario, post-World War II, there followed a lengthy period of time where only minimal infrastructure maintenance occurred. Now, however, most of that infrastructure is approaching the end of its predicted life expectancy, and has started failing at an unprecedented rate. The combination of low maintenance and the increasing age of water distribution infrastructure has resulted in increasing rates of pipe failures. To assign priorities for repair/ replacement, artificial neural network modelling is employed. Eight independent variables are employed, namely pipe length, diameter, age, break category, soil type, pipe material, the year of Cement Mortar Lining (if implemented), and the year of Cathodic Protection (if implemented), to determine the importance of different factors influencing the pipe failure rate. The results in application to the distribution system in Etobicoke, Ontario demonstrate that ANN models have very strong predictive capabilities (R 2 =0.94) when compared with the multiple linear regression method (R 2 =0.75) to assist rehabilitation planning.Après la rapide expansion urbaine qui suivi la seconde guerre mondiale, l'Ontario connu une longue période pendant laquelle on ne porta attention qu'à l'entretien des petites infrastructures. Maintenant, la plupart des infrastructures approchent de leur fin de vie, et ont commencé à se détériorer à un rythme sans précédent. La combinaison du faible entretien et du vieillissement des infrastructures de distribution de l'eau a entraîné une augmentation des taux de bris des conduites. Pour constituer un outil d'aide à la décision, essentiel dans le choix du réseau à réhabiliter en priorité, on cartographie la prévision des défaillances du réseau de distribution d'eau à l'aide du système de modélisation des réseaux neuronaux artificiels (RNA). Cette approche a été appliquée au réseau de la ville de Etobicoke dans l'Ontario. Le modèle comporte huit variables indépendantes, notamment: longueur de la conduite, diamètre, âge, matériau, catégorie des défaillances, type de sol, plus deux facteurs de travaux de réhabilitation. Aux canalisations, on inclut l'année de mortier du ciment de revêtement (s'il a été appliqué), et l'année de la protection cathodique (si elle est appliquée). Afin de déter-miner l'importance des différents facteurs qui influencent les défaillances des conduites. Les résultats obtenus pour le réseau d'eau à Etobicoke, démontrent que les modèles RNA ont de très fortes capacités de prévision (R 2 = 0.94) pour faciliter les stratégies de réhabilitation, par rapport à la méthode de régression linéaire multiple (R 2 = 0.75).

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Section: Precedent Models For Watermain Failurementioning

confidence: 93%

Section: Precedent Models For Watermain Failurementioning

confidence: 99%

Forecasting watermain failure using artificial neural network modelling

Asnaashari

McBean

Gharabaghi

et al. 2013

Canadian Water Resources Journal

View full text Add to dashboard Cite

show abstract

“…Although, Ahn et al (2005) indicate that their model was able to predict the number of breaks, there is insufficient detailed information to assess if the data they present explained the influence of water or soil temperatures on main breaks. Again the analyses were conducted on data collected over a one year period.…”

Section: Review Of Past Studiesmentioning

confidence: 95%

“…The authors claimed that the model was successfully applied to a holdout sample, demonstrating that the ANN "learned" the breakage patterns rather than memorized them 2 . Ahn et al (2005) explored the use of ANN to relate breaks to water and soil temperatures.…”

Section: Review Of Past Studiesmentioning

confidence: 99%

Exploration of the relationship between water main breaks and temperature covariates

Rajani

Kleiner

Sink

2012

Urban Water Journal

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/npsi/ctrl?lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=fr Access and use of this website and the material on it are subject to the Terms and Conditions set forth at http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. For the publisher's version, please access the DOI link below./ Pour consulter la version de l'éditeur, utilisez le lien DOI ci-dessous.http://dx.doi.org/10. 1080/1573062X.2011.630093 Urban Water Journal, 9, 2, pp. 67-84, 2012-05-01 Exploration of the relationship between water main breaks and temperature covariates Rajani, B. B.; Kleiner, Y.; Sink, J-E. Exploration of the relationship between water main breaks and temperature covariatesRajani, B.B.; Kleiner, Y.; Sink, J-E. NRCC-54537A version of this document is published in :Urban Water Journal, 9, (2) This paper examines the impact of temperature changes on observed pipe breakage rate for three pipe materials, namely, cast iron, ductile iron and galvanized steel. Several water and airtemperature-based covariates were tested in conjunction with a non-homogeneous Poisson pipe break model to assess their impact on water main breaks, using data sets from three different water utilities in the USA and Canada. Temperature-based covariates, such as average mean air temperature, maximum air temperature increase and decrease, and how fast the air temperature increase and decrease over a specific period of days, were found to be consistently significant.While the availability of water temperature data (which most utilities do not have) can enhanced the prediction of water main breaks, it appears that air temperature data alone (which most utilities can access) are usually sufficient.Keywords: Water and air temperature-based covariates, impact of temperature on water main breaks, non-homogeneous Poisson probabilistic model.

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“…Reliability of water distribution networks relates to two types of failure, mechanical failure of system components and hydraulic failure caused by changes in demand and pressure head (Tabesh 1998 age (Shamir & Howard 1979), age and diameter (Kettler & Goulter 1985;Giustolisi et al 2006), diameter (Kettler & Goulter 1983;Su et al 1987;Goulter & Kazemi 1988, 1989Mays 1989;Cullinane et al 1992;Goulter et al 1993;Tabesh & Abedini 2005), climatic conditions (Harada 1988;Sacluti 1999;Welter 2001;Ahn et al 2005 (Su et al 1987;Fujiwara & Tung 1991;Cullinane et al 1992;Khomsi et al 1996). All these formulae involve pipe failure rate but, as will be seen in this paper, they produce different results.…”

Section: Introductionmentioning

confidence: 99%

Assessing pipe failure rate and mechanical reliability of water distribution networks using data-driven modeling

Tabesh

Soltani

Farmani

et al. 2009

Journal of Hydroinformatics

132

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In this paper two models are presented based on Data-Driven Modeling (DDM) techniques (Artificial Neural Network and neuro-fuzzy systems) for more comprehensive and more accurate prediction of the pipe failure rate and an improved assessment of the reliability of pipes.Furthermore, a multivariate regression approach has been developed to enable comparison with the DDM-based methods. Unlike the existing simple regression models for prediction of pipe failure rates in which only few factors of diameter, age and length of pipes are considered, in this paper other parameters such as pressure and pipe depth, are also included. Furthermore, an investigation is carried out on most commonly used mechanical reliability relationships and the results of incorporation of the proposed pipe failure models in the reliability index are compared.The proposed models are applied to a real case study involving a large water distribution network in Iran and the results of model predictions are compared with measured pipe failure data.Compared with the results of neuro-fuzzy and multivariate regression models, the outcomes of the artificial neural network model are more realistic and accurate in the prediction of pipe failure rates and evaluation of mechanical reliability in water distribution networks.

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Predicting water pipe breaks using neural network

Cited by 23 publications

References 0 publications

Forecasting watermain failure using artificial neural network modelling

Forecasting watermain failure using artificial neural network modelling

Exploration of the relationship between water main breaks and temperature covariates

Assessing pipe failure rate and mechanical reliability of water distribution networks using data-driven modeling

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