Water Network Assessment and Reliability Analysis by Use of Survival Analysis

A holistic and sustainable strategy for the management of urban water distribution networks should be composed of two equally important pillars: (1) efficient methods for monitoring, repairing or replacing aging infrastructure, and (2) effective tools for modelling the deterioration in the network and for proactively assessing the risk of failure of its components so as to devise preventive measures for avoiding such failures. The paper presents a framework for devising such a proactive risk-based integrity-monitoring strategy for the management of urban water distribution networks. The framework presented is based on a combination of artificial neural network, parametric and nonparametric survival analysis and it is utilized in the estimation of time-to-failure metrics for pipe networks.

[1]  Parviz Fattahi,et al.  A Compromise Programming Model to Integrated Urban Water Management , 2010 .

[2]  Symeon E. Christodoulou,et al.  A Risk Analysis Framework for Evaluating Structural Degradation of Water Mains in Urban Settings, Using Neurofuzzy Systems and Statistical Modeling Techniques , 2003 .

[3]  Elisa T. Lee,et al.  Statistical Methods for Survival Data Analysis , 1994, IEEE Transactions on Reliability.

[4]  Elisa Lee,et al.  Statistical Methods for Survival Data Analysis: Lee/Survival Data Analysis , 2003 .

[5]  Robert M. Clark,et al.  Water Distribution Systems: A Spatial and Cost Evaluation , 1982 .

[6]  Massoud Tabesh,et al.  A Prioritization Model for Rehabilitation of Water Distribution Networks Using GIS , 2011, Water Resources Management.

[7]  V. A. Epanechnikov.  Non-Parametric Estimation of a Multivariate Probability Density , 1969 .

[8]  E. Kaplan,et al.  Nonparametric Estimation from Incomplete Observations , 1958 .

[9]  Jaroslav Pollert,et al.  Security of Water Supply Systems: from Source to Tap , 2006 .

[10]  J. Klein,et al.  Survival Analysis: Techniques for Censored and Truncated Data , 1997 .

[11]  Sung-Hoon Hong,et al.  Reliability based design of water distribution networks using multi-objective genetic algorithms , 2003 .

[12]  Balvant Rajani,et al.  Using limited data to assess future needs , 1999 .

[13]  Symeon E. Christodoulou,et al.  Statistical Modeling of the Structural Degradation of an Urban Water Distribution System: Case Study of New York City , 2003 .

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

[15]  Suwan Park,et al.  Identifying the hazard characteristics of pipes in water distribution systems by using the proportional hazards model: 1. Theory , 2004 .

[16]  David W. Hosmer,et al.  Applied Survival Analysis: Regression Modeling of Time-to-Event Data , 2008 .

[17]  Uri Shamir,et al.  An Analytic Approach to Scheduling Pipe Replacement , 1979 .

[18]  D. Cox.  Regression Models and Life-Tables , 1972 .

[19]  Symeon E. Christodoulou,et al.  A Neurofuzzy Decision Framework for the Management of Water Distribution Networks , 2010 .

[20]  David H. Marks,et al.  A new methodology for modelling break failure patterns in deteriorating water distribution systems: Theory , 1987 .

[21]  I. C. Goulter,et al.  Spatial and temporal groupings of water main pipe breakage in Winnipeg , 1988 .

[22]  Humberto Varum,et al.  A Theory of Vulnerability of Water Pipe Network (TVWPN) , 2010 .