Level of Service-Based Asset Management Framework for Water Supply Systems
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 11, Issue 3
Abstract
Operators of North America’s potable water systems are facing numerous challenges in meeting the current needs and future expectations. Even though utility management experts started building customer-driven asset management systems to prioritize the water mains’ maintenance and replacement, the gap between the utility experts’ and end users’ perspectives still exists due to the lack of technical knowledge in terms of assessing the water quality. Therefore, this paper proposes a service-based asset management framework that evaluates the factors associated with the level of service (LOS) of the water supply networks and maps it to the physical condition. In this paper, the LOS for water supply networks is an indicator that measures the ability of a municipality to continuously supply the end users with adequate water quality to ensure fewer customer complaints and higher end-user satisfaction. The framework revolves around three phases: (1) data collection; (2) model implementation, which comprises LOS assessment and LOS and condition mapping models; and (3) results and analysis. To assess the LOS, a questionnaire was designed and analyzed using the best-worst method. Furthermore, an artificial neural network model was developed to map the relationship between the LOS and condition. Water quality, customer complaints, pressure, and continuity of water supply were used as mapping metrics between the LOS and condition. Toward the end, the framework was applied to the water distribution network of Montreal, Canada and it showed promising results in estimating the corresponding LOS from the condition. In addition, a cross-validation was carried out and the results displayed an 0.871 coefficient of determination (), which implies a strong existing relationship between the model inputs and outputs. This framework enables the utility experts to understand the customer perception of the service, optimize the budget allocation, and forecast the LOS based on the network condition.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request (artificial neural network dataset for the pipes sections).
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Published In
Journal of Pipeline Systems Engineering and Practice
Volume 11 • Issue 3 • August 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Jul 19, 2017
Accepted: Feb 6, 2020
Published online: Apr 29, 2020
Published in print: Aug 1, 2020
Discussion open until: Sep 29, 2020
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