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1. Water Leakage Detection Using Neural Networks

   https://doi.org/10.1061/9780784483466.096  

   Sabu, Shreya; Mahinthakumar, Gnanamanikam; Ranjithan, Ranji; Levis, James; Brill, Downey (June 2021, World Environmental and Water Resources Congress 2021)

   null (Ed.)

   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. 

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2. Open capillary siphons

   https://doi.org/10.1017/jfm.2021.1056  

   Wang, Kaizhe; Sanaei, Pejman; Zhang, Jun; Ristroph, Leif (February 2022, Journal of Fluid Mechanics)

   Flow in the inverted U-shaped tube of a conventional siphon can be established and maintained only if the tube is filled and closed, so that air does not enter. We report on siphons that operate entirely open to the atmosphere by exploiting surface tension effects. Such capillary siphoning is demonstrated by paper tissue that bridges two containers and conveys water from the upper to the lower. We introduce a more controlled system consisting of grooves in a wetting solid, formed here by pressing together hook-shaped metallic rods. The dependence of flux on siphon geometry is systematically measured, revealing behaviour different from the conventional siphon. The flux saturates when the height difference between the two container's free surfaces is large; it also has a strong dependence on the climbing height from the source container's free surface to the apex. A one-dimensional theoretical model is developed, taking into account the capillary pressure due to surface tension, pressure loss due to viscous friction, and driving by gravity. Numerical solutions are in good agreement with experiments, and the model suggests hydraulic interpretations for the observed flux dependence on geometrical parameters. The operating principle and characteristics of capillary siphoning revealed here can inform biological phenomena and engineering applications related to directional fluid transport. 

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3. Effects of density and slope angle on internal erosion in an unsaturated clayey sand slope

   https://doi.org/10.17603/ds2-c5sm-1c95  

   Afolayan, Olaniyi; Lancaster, Anna; Montgomery, Jack (January 2025, Designsafe-CI)

   Soil piping (concentrated leak erosion) is a major contributor to soil erosion in many parts of the world, and collapse of eroded pipes can result in the formation of gullies and sinkholes or trigger slope instability. Despite these significant impacts, there is little understanding of factors controlling pipe collapse, and how water within the pipe influences moisture levels within a slope. In this study, physical models were employed on unsaturated model slopes with pre-formed macropores to investigate how soil properties, pipe characteristics, and hydraulic conditions govern internal erosion processes and slope stability. Experiments simulated shallow field conditions (0.45 m overburden) using 4 mm and 12 mm pipes to establish preferential flow paths, while varying model parameters including initial compaction moisture content and density, pipe condition (absent, closed, or open), slope angle, and model width. Volumetric water content sensors monitored moisture evolution, while cameras captured slope responses to subsurface flow. Results demonstrate that initial compaction conditions (water content and density), pipe size, hydraulic connectivity, and pipe condition control internal erosion processes and slope stability. 

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4. Supervised machine learning approaches for leak localization in water distribution systems: impact of complexities of leak characteristics

   Basnet, Lochan; Bril, Downey E; Ranjithan, Ranji S; Mahinthakumar, Kumar (May 2023, Journal of water resources planning and management)

   Localizing pipe leaks is a significant challenge for water utilities worldwide. Pipe leaks in water distribution systems (WDSs) can cause the loss of a large amount of treated water, leading to pressure loss, increased energy costs, and contamination risks. What makes localizing pipe leaks challenging is the underground location of the water pipes and the similarity in impact on hydraulic properties (e.g., pressure, flow) due to leaks as compared to the effects of WDS operational changes. Physical methods to locate leaks are expensive, intrusive, and heavily localized. Computational approaches such as data-driven machine learning models provide an economical alternative to physical methods. Machine learning models are readily available and easily customizable to most problems; therefore, there is an increasing trend in their application for leak localization in WDSs. While several studies have applied machine learning models to localize leaks in single pipes and small test networks, these studies have yet to thoroughly test these models against the different complexities of leak localization problems, and hence their applicability to real-world WDSs is still unclear. The simplicity of the WDSs, the oversimplification of leak characteristics, and the lack of consideration of modeling and measuring device uncertainties adopted in most of these studies make the scalability of their proposed approaches questionable to real-world WDSs. Our study addresses this issue by devising four study cases of different complexity that account for realistic leak characteristics and model- and measuring device-related uncertainties. Two established machine learning models—multilayer perceptron (MLP) and convolutional neural network (CNN)—are trained and tested for their ability to localize the leaks and predict their sizes for each of the four study cases using different simulated hydraulic inputs. In addition, the potential benefit of combining different types of hydraulic data as inputs to the machine learning models in localizing leaks is also explored. Pressure and flow, two common hydraulic measurements, are used as inputs to the machine learning models. Further, the impact of single and multiple time point input in leak localization is also investigated. The results for the L-Town network indicate good accuracies for both the models for all study cases, with CNN consistently outperforming MLP. 

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5. Experimental Investigation of Steel-Borne Acoustic Pulses for Fault Pinpointing in Pipe-Type Cable Systems: A Scaled-Down Model Approach

   https://doi.org/10.3390/s24217043  

   Moutassem, Zaki; Li, Gang; Zhu, Weidong (November 2024, Sensors)

   Pipe-type cable systems, including high-pressure fluid-filled (HPFF) and high-pressure gas-filled cables, are widely used for underground high-voltage transmission. These systems consist of insulated conductor cables within steel pipes, filled with pressurized fluids or gases for insulation and cooling. Despite their reliability, faults can occur due to insulation degradation, thermal expansion, and environmental factors. As many circuits exceed their 40-year design life, efficient fault localization becomes crucial. Fault location involves prelocation and pinpointing. Therefore, a novel pinpointing approach for pipe-type cable systems is proposed, utilizing accelerometers mounted on a steel pipe to capture fault-induced acoustic signals and employing the time difference of arrival method to accurately pinpoint the location of the fault. The experimental investigations utilized a scaled-down HPFF pipe-type cable system setup, featuring a carbon steel pipe, high-frequency accelerometers, and both mechanical and capacitive discharge methods for generating acoustic pulses. The tests evaluated the propagation velocity, attenuation, and pinpointing accuracy with the pipe in various embedment conditions. The experimental results demonstrated accurate fault pinpointing in the centimeter range, even when the pipe was fully embedded, with the acoustic pulse velocities aligning closely with the theoretical values. These experimental investigation findings highlight the potential of this novel acoustic pinpointing technique to improve fault localization in underground systems, enhance grid reliability, and reduce outage duration. Further research is recommended to validate this approach in full-scale systems. 

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