[No Abstract Available] / Applied Science, Faculty of / Mechanical Engineering, Department of / Graduate

Water distribution utilities are responsible for supplying both clean and safe drinking water, while under constraints of operating at an efficient and acceptable performance level. The City of Kingston, Ontario is currently experiencing elevated costs to repair its aging buried water main assets. Utilities Kingston is opting for a more efficient and practical means of forecasting pipe breaks and the application of a predictive water main break models allows Utilities Kingston to forecast future pipe failures and plan accordingly. The objective of this thesis is to develop an artificial neural network (ANN) model to forecast pipe breaks in the Kingston water distribution network. Data supplied by Utilities Kingston was used to develop the predictive ANN water main break model incorporating multiple variables including pipe age, diameter, length, and surrounding soil type. The constructed ANN model from historical break data was utilized to forecast pipe breaks for 1-year, 2-year, and 5-year planning periods. Simulated results were evaluated by statistical performance metrics, proving the overall model to be adequate for testing and forecasting. Predicted breaks were as follows, 33 breaks for 2011-2012, 22 breaks for 2012-2013 and 35 breaks for 2013-2016. Additionally, GIS plots were developed to highlight areas in need of potential rehabilitation for the distribution system. The goal of the model is to provide a practical means to assist in the management and development of Kingston’s pipe rehabilitation program, and to enable Utilities Kingston to reduce water main repair costs and to improve water quality at the customer's tap. / Thesis (Master, Civil Engineering) -- Queen's University, 2013-10-21 15:30:10.288

Water plays a critical role in every aspect of civilization: agriculture, industry, economy, environment, recreation, transportation, culture, and health. Much of America's drinking water and wastewater infrastructure; however, is old and deteriorating. A crisis looms as demands on these systems increase. The costs associated with renewal of these aging systems are staggering. There is a critical disconnect between the methodological remedies for infrastructure renewal problems and the current sequential or isolated manner of renewal analysis and execution. This points to the need for a holistic systems perspective to address the renewal problem. Therefore, new tools are needed to provide support for wastewater infrastructure decisions. Such decisions are necessary to sustain economic growth, environmental quality, and improved societal benefits. Accurate prediction of wastewater pipe structural and functional deterioration plays an essential role in asset management and capital improvement planning. The key to implementing an asset management strategy is a comprehensive understanding of asset condition, performance, and risk profile. The primary objective of this research is therefore to develop protocols and methods for evaluating the wastewater pipe performance. This research presents the life cycle of wastewater pipeline identifying the causes of pipe failure in different phases including design, manufacture, construction, operation and maintenance, and repair/rehabilitation/replacement. Various modes and mechanisms of pipe failure in wastewater pipes were identified for different pipe material which completed with results from extensive literature reviews, and interviews with utilities and pipe associations. After reviewing all relevant reports and utility databases, a set of standard pipe parameter list (data structure) and a pipe data collection methodology were developed. These parameters includes physical/structural, operational/functional, environmental and other parameters, for not only the pipe, but also the entire pipe system. This research presents a development of a performance index for wastewater pipes. The performance index evaluates each parameter and combines them mathematically through a weighted summation and a fuzzy inference system that reflects the importance of the various factors. The performance index were evaluated based on artificial data and field data to ensure that the index could be implemented to real scenarios. Developing a performance index led to the development of a probabilistic performance prediction model for wastewater pipes. A framework would enable effective and systematic wastewater pipe performance evaluation and prediction in asset management programs. / Ph. D.