Document Type: Research Paper
A water distribution network is one of the important parts of infrastructure systems. The efficient management and proactive planning of capital investment of these assets are fundamental for efficient and effective service delivered by water companies. The direct economic costs (i.e. rehabilitation investment, repair costs, water loss, etc.) as well as indirect costs (i.e. service and traffic interruptions, etc.) related to water pipe bursts are rapidly increasing. The ability to predict burst rate in pipes is an important strategic key in order to optimization of rehabilitation decision in water distribution systems. Most networks suffer from lack of enough and reliable data for bursts and failures. In this study basic variables which influence on pipes burst and burst statistical analysis have been identified and evaluated. Then common methods for burst predicting are discussed. In order to identify logical, useful and understandable patterns of breaks data, a data mining methodology named evolutionary polynomial regression (EPR) is described. Starting from a hybrid evolutionary strategy, EPR searches for patterns in data and returns symbolic expressions/models. This approach is demonstrated through a detailed case study. Required data were collected from the Mashhad Water Company which includes both asset and bursts data recorded for year 1384. The whole database was divided into 8 material/diameter classes (from 64 mm to 300 mm). The resulting models for burst prediction in different zones contain explicitly recognizable independent variables. The expression models confirm that pipe age, diameter and length are the most important variables leading to pipe bursts. Also the effects of pressure on pipe burst prediction were implicitly investigated. It was found that pressure is an important parameter which influences number of breaks in a pipe network.