Pressure as a predictor of occurrence of pipe breaks in water distribution networks

Sudden bursts in water distribution networks may lead to costly consequences. As pressure is one of the causes of such events, pressure management could reduce the probability of failure. In this work, a methodology is proposed to analyse the relationship between pipe breaks and water pressure by means of pressure-related indicators. The objective is to identify the most influential indicators for the probability of occurrence of pipe breaks. The methodology compares the cumulative distribution function (CDF) conditioned to breaks and 100 random sets of the same size sampled from the CDF of the indicator. The most influential indicators are related to the greatest number of rejected cases of the Kolmogorov-Smirnov test. Moment indicators and their calculation period emerge from sensitivity analyses. The methodology is applied to six sectors of Madrid (Spain) while two sectors are used for validation. Results show that the pressure range is the best indicator of breaks.

[1]  V. K. Kanakoudis,et al.  Assessing the Performance Level of a Water System , 2004 .

[2]  S. Tsitsifli,et al.  Developing appropriate performance indicators for urban water distribution systems evaluation at Mediterranean countries , 2012 .

[3]  H. Muhammetoglu,et al.  Urban Water Pipe Networks Management Towards Non‐Revenue Water Reduction: Two Case Studies from Greece and Turkey , 2014 .

[4]  J Thornton,et al.  Progress in practical prediction of pressure: leakage, pressure: burst frequency and pressure: consumption relationships , 2005 .

[5]  Andrew J. Day,et al.  Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system , 2003, Inf. Fusion.

[6]  Seth D. Guikema,et al.  Statistical models for the analysis of water distribution system pipe break data , 2009, Reliab. Eng. Syst. Saf..

[7]  F. J. Arregui,et al.  Burst Detection in Water Networks Using Principal Component Analysis , 2012 .

[8]  A. O. Lambert,et al.  Recent Developments in Pressure Management , 2010 .

[9]  Vladan Babovic,et al.  A Data Mining Approach to Modelling of Water Supply Assets , 2002 .

[10]  Colin Christian,et al.  Bayesian-based pipe failure model , 2004 .

[11]  R. A. Groeneveld,et al.  Practical Nonparametric Statistics (2nd ed). , 1981 .

[12]  Luigi Berardi,et al.  Development of pipe deterioration models for water distribution systems using EPR , 2008 .

[13]  Vasilis Kanakoudis,et al.  A troubleshooting manual for handling operational problems in water pipe networks , 2004 .

[14]  David J. Groggel,et al.  Practical Nonparametric Statistics , 2000, Technometrics.

[15]  Vasilis Kanakoudis,et al.  Water pipe network reliability assessment using the DAC method , 2011 .

[16]  Vasilis Kanakoudis,et al.  Predicting the behavior of a pipe network using the "critical Z-score" as its performance indicator. , 2010 .

[17]  Jon Røstum,et al.  Statistical modelling of pipe failures in water networks , 2000 .

[18]  Selami Kara,et al.  Implementation of Hydraulic Modelling for Water-Loss Reduction Through Pressure Management , 2012, Water Resources Management.

[19]  Stavroula Tsitsifli,et al.  A new set of water losses-related performance indicators focused on areas facing water scarcity conditions , 2013 .

[20]  Enrique Cabrera,et al.  Performance Indicators for Water Supply Services: Third Edition , 2006 .

[21]  Tarek Zayed,et al.  Study of the Suitability of Existing Deterioration Models for Water Mains , 2009 .

[22]  Vasilis Kanakoudis.  Vulnerability based management of water resources systems , 2004 .

[23]  V. K. Kanakoudis,et al.  The role of leaks and breaks in water networks: technical and economical solutions , 2001 .

[24]  Qiang Xu,et al.  Pipe break prediction based on evolutionary data-driven methods with brief recorded data , 2011, Reliab. Eng. Syst. Saf..

[25]  Marco Fantozzi,et al.  Practical approaches to modeling leakage and pressure management in distribution systems - progress since 2005 , 2013 .