Statistical estimation of water distribution system pipe break risk
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The deterioration of pipes in urban water distribution systems is of concern to water utilities throughout the world. This deterioration generally leads to pipe breaks and leaks, which may result in reduction in the water-carrying capacity of the pipes from tuberculation of interior walls of the pipe. Deterioration can also lead to contamination of water in the distribution systems. Water utilities which are already facing tight funding constraints incur large expenses in replacement and rehabilitation of water mains, and hence it becomes critical to evaluate the current and future condition of the system for making maintenance decisions. Quantitative estimates of the likelihood of pipe breaks on individual pipe segments can facilitate inspection and maintenance decisions. A number of statistical methods have been proposed for this estimation problem. This thesis focuses on comparing these statistical models on the basis of short time histories. The goals of this research are to estimate the likelihood of pipe breaks in the future and to determine the parameters that most affect the likelihood of pipe breaks. The various statistical models reviewed in this thesis are time linear and time exponential ordinary least squares regression models, proportional hazards models (PHM), and generalized linear models (GLM). The data set used for the analysis comes from a major U.S. city, and the data includes approximately 85,000 pipe segments with nearly 2,500 breaks from 2000 through 2005. The covariates used in the analysis are pipe diameter, length, material, year of installation, operating pressure, rainfall, land use, soil type, soil corrosivity, soil moisture, and temperature. The Logistic Generalized Linear Model fits can be used by water utilities to choose inspection regimes based on a rigorous estimation of pipe breakage risk in their pipe network.
Yamijala, Shridhar (2007). Statistical estimation of water distribution system pipe break risk. Master's thesis, Texas A&M University. Available electronically from