Abstract

Considerable capital investment has been made in civil infrastructure systems across the globe including water mains. Currently, existing failure detection and location methods do not allow for quick reaction to failures. In addition, no unified standards are followed in condition assessment of water mains. Moreover, the decision by which water mains are inspected is currently carried out based on approximation and experience of decision-makers, which might be limited and may lead to overlooking suitable evaluation methods that might save time, effort and cost. This research presents a methodology that contributes to different phases in the asset management of water mains. It enhances current practice in condition assessment of water mains and assists in setting up rehabilitation priorities. The methodology implemented is based on intensive literature review, evaluation of current methods, field investigation and experiments, and interviews with experts. The methodology considered augments two approaches currently used in condition assessment of water mains, which are proactive and reactive methods. The developed methodology calls for designing a new decision support system (DSS) for selection of most suitable non-destructive evaluation (NDE) method(s). It consists of two components: 1) A Database management system (DBMS), and 2) an evaluation and ranking module (ERM). These NDE methods are used for either detecting suspected leaks or measuring pipe wall thickness, the latter is employed to predict remaining service life of pipe being considered. In case of suspected leaks, this study presents a newly designed automated system for detection of water leaks in underground pipelines and identifying their respective locations. The development of this system is based on using Thermography infrared (IR) camera in order to detect thermal contrast at the pavement surface due to water leaks at the most suitable time. The data obtained is analyzed in order to establish the relationship between the detected leakage area and the approximate location of leak. Prototype software developed in Visual C# environment is implemented in order to determine the location of leaks automatically. Two deterioration models were designed and developed for estimating remaining useful life of water mains, and predicting annual breakage rate of water mains. The development of the two models is based on the analysis of real data collected from 16 municipalities in Canada and the US. The two models were developed considering two approaches, multiple regression analysis and Artificial Neural Networks (ANNs) based on the most suitable subsets of selected factors. The final model was selected due to its reliability and better performance in comparison to other models. The outputs of the deterioration models developed in this research were used, in addition to other deterioration factors that were not considered in existing models in order to develop a Decision Support System (DSS) for generating condition rating scale of water main being considered, and for prioritizing rehabilitation/ maintenance actions. The system is hierarchal in structure, and the condition-rating index is generated using Multi Attributes Utility Theory (MAUT). System validation was carried out by comparing its outcome with real case studies. A prototype software application of the model presented in this research is implemented as a proof of concept to demonstrate the capabilities and essential features of the developed models.