It is estimated that about 20% of treated drinking water is lost through distribution pipeline leakages in the United States. Pipeline leakage detection is a top priority for water utilities across the globe as leaks increase operational energy consumption and could also develop into potentially catastrophic water main breaks, if left unaddressed. Leakage detection is a laborious task often limited by the financial and human resources that utilities can afford. Many conventional leak detection techniques also only offer a snapshot indication of leakage presence. Furthermore, the reliability of many leakage detection techniques on plastic pipelines that are increasingly preferred for drinking water applications is questionable. As part of a smart water utility framework, this paper proposes and validates a hydraulic model-based technique for detecting and assessing the severity of leakages in buried water pipelines through monitoring of pressure from across the water distribution system (WDS). The envisioned smart water utility framework entails the capabilities to collect water consumption data from a limited number of WDS nodes and pressure data from a limited number of pressure monitoring stations placed across the WDS. A popular benchmark WDS is initially modified by inducing leakages through addition of orifice nodes. The leakage severity ismore »
Water Leakage Detection Using Neural Networks
The primary goal of the project is to leverage recent developments in smart water technologies to detect and reduce water leakages in large water distribution networks with the aid of neural networks. A cost effective non-invasive solution to detect leakages in transmission pipelines is needed by many water utilities as it will lead to significant water savings and reduced pipe breakage frequencies, especially in older infrastructure systems. The eventual goal of the project is to test the ANN model on a real network using field measured pressure and pipe breakage data after tuning and developing the model with simulated data. In this project we propose building a regression model, based on Multi-Layer Perceptron (MLP) algorithm, which is a class of feedforward Artificial Neural Networks (ANNs) to detect the leak locations within a proposed network. The model should be able to learn the structure, i.e. mapping of various leak nodes and sensor nodes in an area, such that it can detect the leak nodes based on the pressure values with significant accuracy.
- Award ID(s):
- 1919228
- Publication Date:
- NSF-PAR ID:
- 10282254
- Journal Name:
- World Environmental and Water Resources Congress 2021
- Page Range or eLocation-ID:
- 1033 to 1040
- Sponsoring Org:
- National Science Foundation
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Water distribution systems (WDSs) face a significant challenge in the form of pipe leaks. Pipe leaks can cause loss of a large amount of treated water, leading to pressure loss, increased energy costs, and contamination risks. Locating pipe leaks has been a constant challenge for water utilities and stakeholders due to the underground location of the pipes. Physical methods to detect leaks are expensive, intrusive, and heavily localized. Computational approaches provide an economical alternative to physical methods. Data-driven machine learning-based computational approaches have garnered growing interest in recent years to address the challenge of detecting pipe leaks in WDSs. While several studies have applied machine learning models for leak detection on single pipes and small test networks, their applicability to the real-world WDSs is unclear. Most of these studies simplify the leak characteristics and ignore modeling and measuring device uncertainties, which makes the scalability of their approaches questionable to real-world WDSs. Our study addresses this issue by devising four study cases that account for the realistic leak characteristics (multiple, multi-size, and randomly located leaks) and incorporating noise in the input data to account for the model- and measuring device- related uncertainties. A machine learning-based approach that uses simulated pressure asmore »
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Abstract This work investigates the siphon break phenomenon associated with pipe leakage location. The present study is divided into two parts: (1) an unsteady three-dimensional (3D) computational fluid dynamics (CFD) model is developed to simulate the pressure head (water level) and discharge in the simulated siphon using the volume-of-fluid (VOF) technique under no-leakage condition and (2) using the model developed in the first part we investigated the siphon break phenomenon associated with pipe leakage location. The calculated results of transient water level and discharge rate at the simulated siphon for the no-leakage condition were in good agreement with the experimental measurements. In addition, the velocity, pressure fields, and phase fractions in the siphon pipe were analyzed in depth. The methodology and findings presented show that leakage above the hydraulic grade line and close to the top inverted U section of the siphon pipe ultimately leads to the siphon breakage, which is not the case for a leakage below the hydraulic grade line. It is also concluded that if leakage is above the hydraulic grade line and the leakage position is far away from the upper horizontal section of the siphon pipe, it may not lead to the immediate siphon breakagemore »
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1061/9780784483466.096
