Water Leakage Detection System Using Arduino

Clean water is a scarce resource for the human life and is subject to wastage due to leakage of the distribution pipes in large cities. Water pipe leakage is a big problem around the world of which most of the water distribution authorities faces difficulties to detect the location of the fault. This problem of leakage can be caused by several factors such as breakage of the pipelines due to aging or ongoing constructions in urban cities like Dar es salaam, consequently due to that case, the distribution authorities face hardship to identify the cause and enable them to take action. Therefore, the aim of this project was to develop an IoT-based system for water leakage detection. The prototype was developed comprising two sensors embedded at the source and destination points to measure the flow rate of water. The result indicated that the volume of water generated at the start point can be compared with the other end to determine if there is any leakage. A greater focus on distance calculation could produce interesting findings that account for more research on IoT monitoring systems.

Research on the Evaluation of Service Status of Tunnel Structure Near the Station

To study the service status of the tunnel structure near the station, a disease detection of Metro Line 1 near the station during the operation period was carried out. The four major tunnel structural diseases including water leakage, segment stagger, open joints, and lining cracks were counted. The distribution law of the structural diseases along the longitudinal and circumferential directions of the interval tunnel was analyzed. The service status of segment components and connecting bolts were evaluated based on existing specification. The overall service status of the tunnel was determined. The service status level of the upstream line was determined as “iii”, and the service status was “degradation”, the service status level of the downstream line was determined as “iv” and the service status was “deteriorated”.

Leakage analysis of campus water supply system based on MLP neural network

Water supply system is an important part of campus public facilities, and the water supply pipeline will leak, not only increase the cost of water supply, but also cause water waste. This paper collects water consumption data of water meters at all levels in a school’s water supply system, establishes MLP multilayer perceptron neural network model, determines the water leakage rate according to the fluctuation of predicted value and actual value, so as to analyze the leakage situation of each school’s water supply system. When the fluctuation between the actual and predicted water consumption exceeds a certain threshold, water leakage occurs on that day. Through solving the model, the following conclusions are finally drawn: (1) the annual water leakage rate of the school is 10.74%, and the water leakage is 29131.418L. (2) The water leakage rate in the first quarter is the highest, and the water leakage in the second quarter is the highest.(3) The school aquaculture area is the most serious leakage phenomenon, and the maximum water leakage rate of each water meter node is more than 10%.

Air, helium and water leakage in rubber O-ring seals with application to syringes

We study the leakage of fluids (liquids or gases) in syringes with glass barrel, steel plunger and rubber O-ring stopper. The leakrate depends on the interfacial surface roughness and on the viscoelastic properties of the rubber. Random surface roughness is produced by sandblasting the rubber O-rings. We present a very simple theory for gas flow which takes into account both the diffusive and ballistic flow. The theory shows that the interfacial fluid flow (leakage) channels are so narrow that the gas flow is mainly ballistic (the so called Knudsen limit). We compare the leakrate obtained using air and helium. For barrels filled with water we observe no leakage even if leakage occurs for gases. We interpret this as resulting from capillary (Laplace pressure or surface energy) effects.

Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems

This paper presents an investigation of the capacity of machine learning methods (ML) to localize leakage in water distribution systems (WDS). This issue is critical because water leakage causes economic losses, damages to the surrounding infrastructures, and soil contamination. Progress in real-time monitoring of WDS and ML has created new opportunities to develop data-based methods for water leak localization. However, the managers of WDS need recommendations for the selection of the appropriate ML methods as well their practical use for leakage localization. This paper contributes to this issue through an investigation of the capacity of ML methods to localize leakage in WDS. The campus of Lille University was used as support for this research. The paper is presented as follows: First, flow and pressure data were determined using EPANET software; then, the generated data were used to investigate the capacity of six ML methods to localize water leakage. Finally, the results of the investigations were used for leakage localization from offline water flow data. The results showed excellent performance for leakage localization by the artificial neural network, logistic regression, and random forest, but there were low performances for the unsupervised methods because of overlapping clusters.