Development of a predictive model for failure rates in PVC pipes based on pipe locations and diameter.

Abstract

This study investigates the development of a predictive model for failure rates in Polyvinyl Chloride (PVC) water distribution pipes using existing data on pipe locations and diameters. Understanding and predicting pipe failures are essential for proper infrastructure management, avoiding water loss, and optimizing maintenance schedules. This study strives to identify statistically significant correlations between pipe locations and diameters, and number of failures encountered. Historical records of failures of PVC pipes, combined with geographical information system (GIS) data on pipe networks and their surrounding environment, will be statistically modeled using modeling techniques such as regression analysis or machine learning algorithms. The predictive model will provide a valuable tool for water utilities to predict high risk pipe sections, there by implementing specific inspections and replacement policies consequently saving on operating costs and improving service quality. The findings of this study conducted in the context of Uganda's water infrastructure will shed light on the specific variables influencing failures in PVC pipes in this country and help advance general knowledge regarding water distribution network asset management