Abstract
AbstractThe occurrence of bursts in water pipelines can not only prevent the system from functioning properly, but it can also produce significant water loss that disrupts activities in urban areas...
Highlights
Water transmission and distribution pipelines are critical infrastructure for modern cities
Considering the multiple steps involved in the model development stage (Step A.8 in Fig. 2), samples of numerical transient head traces were generated at random distances on average every 0.2 m along the pipeline to complete 5,000 transient head traces in total
This paper presents a novel technique to detect and characterize the occurrence of bursts in pipelines by merging the use of fluid transient waves and artificial neural networks
Summary
Water transmission and distribution pipelines are critical infrastructure for modern cities. Some water transmission pipelines cover long distances through remote areas that are not inspected on a regular basis To monitor these systems, different noninvasive techniques have been developed to identify events that may put the functioning of a pipeline at risk. Ye and Fenner (2011) proposed the use of an adaptive Kalman filtering process to predict flow (or pressure) in a system at a district meter area (DMA) level This statistical characterization of a dynamic system is able to model the normal hydraulic parameters that are compared to measured data to detect the occurrence of bursts. These techniques are successful in detecting the occurrence of bursts, they are unable to accurately pinpoint the location of bursts and their range of effectiveness is often limited to a DMA level
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