Moving towards a proactive sewer pipe inspection approach : A state-of-the-art analysis

Abstract: The failure of sewer pipes is a significant issue that can adversely affect the environment and public health. The problem is exacerbated by the additional burden it places on treatment plants, which must work harder to process the increased sewage flow resulting from pipe failures. The research in this thesis is based on an extensive review of the existing literature on sewer pipe failure and inspection, focusing on the proactive approach that can predict pipe failures to assist in effective maintenance. The study finds that several predictive models can accurately predict sewer deterioration with high accuracy (up to 95 % precision), making it possible to identify potential failures and address them before they cause significant damage or disruption. However, the research indicates that there has been relatively little work done on predicting blockage and CSO, two critical aspects of sewer pipe management that could be addressed more to manage sewer systems effectively. The thesis discusses that by developing an effective predictive model for prioritisation of monitoring sewer pipes, planners can save time and money on individual inspections while planning well ahead to avoid any service disruption. The study also summarised the data needs for the predictive models and found pipe age, material, diameter depth, and length to be the most commonly used input parameters by the existing model developers. The finding of this research can guide decision support in future efforts to improve sewer pipe inspection practices.