Statistical models for the analysis of water distribution system pipe break data

The deterioration of pipes leading to pipe breaks and leaks in urban water distribution systems is of concern to water utilities throughout the world. Pipe breaks and leaks may result in reduction in the water-carrying capacity of the pipes and contamination of water in the distribution systems. Water utilities incur large expenses in the replacement and rehabilitation of water mains, making it critical to evaluate the current and future condition of the system for maintenance decision-making. This paper compares different statistical regression models proposed in the literature for estimating the reliability of pipes in a water distribution system on the basis of short time histories. The goals of these models are to estimate the likelihood of pipe breaks in the future and determine the parameters that most affect the likelihood of pipe breaks. The data set used for the analysis comes from a major US city, and these data include approximately 85,000 pipe segments with nearly 2500 breaks from 2000 through 2005. The results show that the set of statistical models previously proposed for this problem do not provide good estimates with the test data set. However, logistic generalized linear models do provide good estimates of pipe reliability and can be useful for water utilities in planning pipe inspection and maintenance.