Statistical modelling of pipe failures in water networks

Abstract

This thesis presents an evaluation of statistical methods for modelling pipe failures for each individual pipe in a water distribution network. This thesis introduces the Non Homogeneous Poisson Process (NHPP) with covariates (i.e. explanatory variables) as an appropriate method for modelling pipe failures in water networks. As part of this research, a computer program has been developed that estimates the parameters in the NHPP (“Power law” model). The results from this NHPP model are compared to the results obtained from a modified Weibull Proportional Hazards Model (PHM), where the hazard function is allowed to continue beyond the pipe’s first failure. The models are applied in a case study using data for the water distribution network in Trondheim, Norway.

The statistical models have been calibrated, verified and used to predict failures for both networks (i.e. group of pipes) and individual pipes. Covariates that have a significant influence on the rate of occurrence of failures (ROCOF) are documented. Based on the results from the case study, NHPP is recommend over the Weibull PHM for modelling failures in water networks.

The output from the statistical models can be used for a variety of purposes in water network management. In the long term the models can be used to estimate future budget needs for rehabilitation. In the short term the models can be used to define candidates for replacement based on poor structural condition. Information about failure intensity is also required for carrying out network reliability analysis. For this purpose reliability data for each individual pipe is required, which is exactly what the predictive models described in this thesis provide.