Simulation-based deterioration patterns of water pipelines

Abstract

Water pipelines deteriorate overtime due to several distressing factors. To keep water pipelines in good condition, municipalities need to use reliable and credible deterioration models and inspection plans to better manage their rehabilitation and maintenance. Thus, this paper presents the development of deterioration models and patterns of water pipelines. The deterioration models consider different water pipe sizes and materials as well as different surrounding environmental conditions which affect their deterioration rates. As a prerequisite to the development of such deterioration models, a condition assessment model for water pipelines was first developed. Questionnaires were distributed among experts to determine the weights of the factors affecting water pipeline conditions using the Fuzzy Analytic Network Process (FANP).Monte-Carlo simulation was used to account for the large uncertainties of the calculated weights in the development of the condition assessment model. The validation of the model, which was performed using historical data, yielded an average validity percentage of 93.59%. The developed models are expected to help municipalities and decision makers to accurately plan for future water pipelines maintenance and rehabilitation activities based on their different deterioration patterns.It takes into consideration both the uncertainties at the initial stage and those accumulated during the calculation process.

Asset management analytics for urban water mains: a literature review

“…Predictive models of water main failures and pipeline deteriorations (Kleiner and Rajani 2001;Rajani and Kleiner 2001;El-Abbasy et al 2019;Robles-Velasco et al 2020;Dawood et al 2020a) may be classified into two main types: a physical law-based model and a data-driven model (Rajani and Kleiner 2001;Snider and McBean 2020a). The first type of model requires significant amounts of input data to analyze physical behaviors leading to a failure.…”

“…This type of model is much less expensive to use, compared to a physical law-based model. Thus, it is suitable to implement a datadriven model to all pipelines, as long as historical data exists (El-Abbasy et al 2019;Snider and McBean 2020a).…”

A review of water quality factors in water main failure prediction models

“…Water hardness is only considered (along with alkalinity and pH) in the aggressiveness index proposed by Hu & Hubble (2007) for cement-based water mains, likely because carbonate has no direct role in metal pipe corrosion. El-Abbasy et al (2019) evaluated the impact of all water quality subfactors (including water hardness) together in a single combined factor. They did this by defining three water quality classespoor, fair and good.…”

“…They did this by defining three water quality classespoor, fair and good. Like Francisque et al (2014), El-Abbasy et al (2019 developed a model to detect and rank the most vulnerable metal and cementitious pipes.…”

Risk-Based Prioritization of Sewer Pipe Inspection from Infrastructure Asset Management Perspective

“…Sewer pipes are repeatedly exposed to surface loading since it is a part of the underground infrastructure; however, measuring the magnitude of surface loading is quite complicated [15]. Thus, many researchers have estimated it with regard to the location where sewer pipes are installed [18,26,31]. The location can be classified based on the corresponding land use, such as industrial, residential and road.…”