Water Leakage Detection Research Articles

Segmentation of tunnel water leakage based on a lightweight DeepLabV3+ model

Abstract The accurate and efficient detection of water leakage with complex backgrounds is crucial for the safety of metro operations. A lightweight segmentation method for metro tunnel water leakage based on transfer learning is proposed. Firstly, this is based on the Deeplabv3+ model and adopts MobileNetv3-Large as the backbone feature extraction network, which significantly reduces the network parameters and improves the detection speed; secondly, it incorporates the efficient channel attention mechanism, which enables the model to adaptively adjust the weights of the channel features and capture the inter-channel relationships in the image, which significantly improves the model’s ability for feature extraction ability; furthermore, for the problem of severe imbalance between positive and negative samples in the dataset, the recognition accuracy of complex samples is increased by optimizing the loss function; finally, the training method of transfer learning is utilized to solve the problem of scarcity of water leakage dataset, and to improve the model’s accuracy and generalization ability. The results show that the model has more significant detection accuracy and segmentation speed advantages than today’s mainstream semantic segmentation model. With strong generalization ability in complex environments (e.g., low illumination and multiple obstructions), the model can be used for intelligent operation and maintenance in metro tunnel projects.

Water Leak Detection: A Comprehensive Review of Methods, Challenges, and Future Directions

This paper provides a comprehensive review of the methods and techniques developed for detecting leaks in water distribution systems, with a focus on highlighting their strengths, weaknesses, and areas for future research. Given the substantial economic, social, and environmental impacts of undetected leaks, timely detection and precise location of leaks are critical concerns for water authorities. This review categorizes existing methods into traditional approaches, such as manual sounding, and modern techniques involving smart water management and sensor technologies. A multidimensional bibliometric analysis was employed to systematically identify, select, and evaluate 600 scholarly articles on water leak detection, sourced from the Scopus database over a 23-year period (2000–2023). The paper evaluates each method based on leak sensitivity, burst detection, continuous monitoring, alarm accuracy, and implementation costs. Novel insights include an analysis of emerging smart water technologies and their integration into real-world water distribution networks, offering improved efficiency in leak detection. The paper also identifies key gaps in current research and suggests future directions for advancing the accuracy and cost-effectiveness of these technologies.

A two-phase approach for leak detection and localization in water distribution systems using wavelet decomposition and machine learning

Water is a crucial resource for all forms of life, yet it is becoming increasingly scarce. A significant portion of water loss in urban and industrial areas is attributed to leaks. Addressing this issue is critical for enhancing efficiency, sustainability, and resource conservation. This paper presents a novel two-phase approach for leak detection and localization in water distribution systems using wavelet decomposition and machine learning for depth analysis of pressure signals. The first phase, Leak Detection, utilizes wavelet analysis to extract significant features from the daily pressure signal data. These features are then inputted into a Random Forest classifier, achieving a classification accuracy of 99% for distinguishing between “Leak” and “No Leak” scenarios. Following the detection, the Leak Localization phase aims to pinpoint the leak’s location using strategically placed sensors within the system. To facilitate understanding and application of our methodology, we have developed a user-friendly, web-based application designed for the detection and localization of water leaks on any given day. Extensive testing in a WDS named “L-Town” has validated our system’s ability to accurately identify leaks. The combination of wavelet-based signal analysis and the Random Forest algorithm forms an effective framework for advanced leak detection in water distribution systems. This approach holds great promise for future research and practical implementations in water management.

Identifying sulphurous water discharge from legacy oil and gas wells using spectral band analysis of aerial and satellite imagery

Legacy oil and gas wells are a significant source of hydrogen sulphide and methane gas release to the atmosphere. Unanticipated occurrences of gas release in urbanized areas can pose human health risks. Several high hydrogen sulphide-emitting wells have been identified in Norfolk County, Ontario, Canada, based on the remote detection of sulphurous water discharging to the surface along the wellbore. Although oil and gas well records report well status and location, community reports of noxious hydrogen sulphide odours suggest potentially compromised well seals and inaccuracies in their documented location. Given the broad-scale nature of this issue and limitations in site access, a remote-sensing based methodology utilizing satellite and high-resolution aerial photography could support identification of undocumented sulphurous water leaks associated with compromised oil and gas wells and subsequent characterization of hydrological and ecohydrological impacts over time. This study presents a multifaceted approach to identifying sulphurous leaking wells utilizing complimentary remote sensing imagery and image analysis tools within ArcGIS. Southwestern Ontario Orthophotography Project air photos and Sentinel-2 satellite imagery were determined to be the most applicable sources of imagery based on their respective advantages in resolution and revisit time. The application of a normalized difference vegetation index showed that major well leaks could exhibit signs of vegetative scarcity, while minor well leaks may have only a limited impact on vegetation. A band combination utilizing the green and near infrared bands was created to enhance the detection of sulphurous water leaks. Utilizing the identified band combination, a pair of potentially undiscovered leaks were located west of a fluvial river channel. Continued research should attempt to employ an automated approach for discerning additional sulphurous water leaks in the region or at different points in time. Possible techniques may involve the deep learning object and change detection models incorporated in ArcGIS.

Characterization of leakage signatures in buried water pipes by ground penetrating radar(GPR) and instantaneous frequency analysis

The aging and complexity of underground water pipes pose significant challenges to modern society, necessitating long-term monitoring and maintenance to prevent the socioeconomic costs. So, effective nondestructive and geophysical methods are needed to localize the possible leakage areas before any rational opening up is conducted. In this study, we propose a workflow to generate instantaneous frequency slices (IFS) from raw ground penetrating radar (GPR) data. Characterizing the horizontal and vertical patterns of water leakage using IFS revealed two key mechanisms—reflections due to the dry and wet interface and absorption due to water distributed in soil—that significantly influence instantaneous frequency. A well-designed real site (Q-Leak), in collaboration with the Water Supplies Department (WSD) of the government, was conducted to replicate typical leakage scenarios, validating the effectiveness of our IFS-based approach. The results of proposed method demonstrate that IFS is a reliable approach for characterizing leakages in buried water pipes, effectively addressing several challenges associated with amplitude slice analysis. Our proposed methodology enriches the toolkit available for large-scale, future-oriented water leakage detection.

Detecting Cracks in Intelligent Carbon-Based TRC Elements Through Wetting Events

Textile reinforced concrete (TRC) technology enables to construct sustainable and environmentally friendly thin-walled structural elements with self-sensory capabilities. The self-sensory concept is based on using high-strength and electrically conductive yarns that are simultaneously used as the main reinforcement system and as the sensory agent. By connecting the two ends of the yarns to either direct current (DC) or alternating current (AC) electrical circuits, it is possible to measure changes in the electrical readings and to further correlate them to the structural health. Studies in the literature validated the concept by using carbon yarns as hybrid monitoring agents. Most of them provided integrated assessments of global structural health but lacked the capability to pinpoint individual cracks. The current study offers a new identification procedure to locate cracks and to estimate their severity by combining two advanced monitoring procedures, that is by adopting the Time Domain Reflectometer (TDR) analysis; and by conceptually using the smart water leakage detection method. Both methods take advantage that a pair of carbon yarns can be positioned parallel to each other and connected to the DAQ system. In case of TDR method, one yarn functions as the signal transmitter and the other as insulation. In case of water leakage detection method, the electrical linkage between the two yarns changes the electrical characterization of the electrical circuit and enables the monitoring capabilities. The proposed study argues that a new identification procedure that benefits from each method can be developed and yields to smart identification procedure that has the capability to locate the crack and estimate its severity. The study will demonstrate the feasibility of the new procedure by experimental study. Results from this study will take a significant step toward developing reliable smart carbon-based TRC structures.

Experimental Study Employing EMI-based Piezoelectric Diaphragms to Detect Water Leakages in PVC Pipes

Considering the increased demand for using natural resources sustainably, water is an ultra-valuable resource to be preserved. Notwithstanding, Polyvinyl Chloride (PVC) pipes, which are mainly used in complex building water distribution systems, are still in use and present a considerable rate of water loss. Unfortunately, the recently developed Structural Health Monitoring (SHM) has not deeply covered leaks in those pipes. To fill this gap, this approach proposes an experimental study to assess the feasibility of using Lead Zirconate Titanate (PZT) diaphragms to detect water leaks in these structures. Experimental tests were conducted by attaching one PZT diaphragm to a mini-scale water distribution system, where the leakage scenarios were simulated by opening taps. To attest to the effectiveness of the methodology, it calculated statistical metrics between the baseline (without leaks) and unknown conditions. The experimental results are promising and may be extrapolated to detect water losses in a full-scale water-distributing system.

Application of Network Reconstruction Algorithm to Compute Maximum Flow for Water Supply Network: A Case Study of the City of Bulawayo, Zimbabwe

Determining the maximum flow value in Water Supply Networks (WSN) is a common problem that is being faced by many cities during and after designing of WSN. In this article, the network reconstruction (NR) algorithm is applied to compute the maximum flow value for the city’s WSN based on the actual data. The city of Bulawayo has been selected for the following reasons, availability of research data, the city is facing severe water shedding and several studies have focused only on other issues such as alternative water sources, leakage detection and demand forecasting. The goal of this study is to determine the maximum flow value from the sources to the reservoirs. The results have revealed that the computed maximum flow value is within the estimated range of 110,000 cubic meters to 190,000 cubic meters. Dam sensitivity analysis were considered to determine the sources to give maintenance priority before the rain season. Several recommendations were suggested to improve the water supply situation.

Development and characterization of log periodic feedline based filter for water leakage detection in size invariant PVC pipes

In this proposed work, water leakage detection in Polyvinyl chloride (PVC) pipes has been investigated using a ring filter connected to log-periodic feedlines (LPF). The proposed filter has a well-structured ring design to adjust the PVC pipes and operates between 0.3 GHz & 0.7 GHz. Using a signal generator & analyzer, attenuation measurements are obtained to analyze the impact of water leakage. PVC pipes with varying diameters (3 inches, 2 inches, 1.5 inches, & 1 inch) are connected with water stoppers to control the water flow during experiments. The leak is contained within a plastic tub to make measurements easier. Results show a good connection between attenuation & water level for all pipe diameters, supporting the LPF-based ring filter’s effectiveness as a leakage detector. An analogous circuit model is created to evaluate the filter’s performance further. At the same time, the resonant parameters, such as Q-factors & resonant frequencies, are retrieved from S21 magnitude measurements in the operating frequency range. The proposed methodology is shown to be accurate & reliable, offering significant potential for water leakage detection in PVC pipe systems. By accurately detecting water leakage, this technology enables prompt repairs, reducing water loss & associated costs, enhancing water infrastructure, and promoting sustainable water management.

IOT Enabled Smart Washroom

While progress is undoubtedly being made in the cutting-edge globe, our country's cleanliness is in jeopardy. The purpose of this paper is to supply hygienic and clean restrooms. Every public restroom ought to be hygienic and spotless. The government of our nation launched the "Swachh Bharat" (Clean India) program. One of the goals of the Clean India initiative is to maintain uncontaminated toilets. This study has the potential to support the clean India initiative. It may eventually demonstrate the major role in the Clean India Plan. The smart bathroom system integrates features like an automatic water tap for hands-free operation, water leakage detection to prevent waste, and an exhaust fan with odour sensing for improved air quality. It dispenses auto air freshener based on bad smell detection linked to overall air quality monitoring. Users can control and monitor these functions through a mobile app. Additionally, an auto-cleaning mechanism activates after every 10 entries to ensure a hygienic space. This system aims to enhance efficiency and convenience in the bathroom environment.

Intelligent Detection of Tunnel Leakage Based on Improved Mask R-CNN

The instance segmentation model based on deep learning has addressed the challenges in intelligently detecting water leakage in shield tunneling. Due to the limited generalization ability of the baseline model, occurrences of missed detections, false detections, and repeated detections are encountered during the actual detection of tunnel water leakage. This paper adopts Mask R-CNN as the baseline model and introduces a mask cascade strategy to enhance the quality of positive samples. Additionally, the backbone network in the model is replaced with RegNetX to enlarge the model’s receptive field, and MDConv is introduced to enhance the model’s feature extraction capability in the edge receptive field region. Building upon these improvements, the proposed model is named Cascade-MRegNetX. The backbone network MRegNetX features a symmetrical block structure, which, when combined with deformable convolutions, greatly assists in extracting edge features from corresponding regions. During the dataset preprocessing stage, we augment the dataset through image rotation and classification, thereby improving both the quality and quantity of samples. Finally, by leveraging pre-trained models through transfer learning, we enhance the robustness of the target model. This model can effectively extract features from water leakage areas of different scales or deformations. Through instance segmentation experiments conducted on a dataset comprising 766 images of tunnel water leakage, the experimental results demonstrate that the improved model achieves higher precision in tunnel water leakage mask detection. Through these enhancements, the detection effectiveness, feature extraction capability, and generalization ability of the baseline model are improved. The improved Cascade-MRegNetX model achieves respective improvements of 7.7%, 2.8%, and 10.4% in terms of AP, AP0.5, and AP0.75 compared to the existing Cascade Mask R-CNN model.

Deep learning based water leakage detection for shield tunnel lining

Deep learning based water leakage detection for shield tunnel lining

Vibration-based water leakage detection system for public open data platforms

Vibration-based water leakage detection system for public open data platforms

Indoor Infrastructure Maintenance Framework Using Networked Sensors, Robots, and Augmented Reality Human Interface

Sensing and cognition by homeowners and technicians for home maintenance are prime examples of human–building interaction. Damage, decay, and pest infestation present signals that humans interpret and then act upon to remedy and mitigate. The maintenance cognition process has direct effects on sustainability and economic vitality, as well as the health and well-being of building occupants. While home maintenance practices date back to antiquity, they readily submit to augmentation and improvement with modern technologies. This paper describes the use of networked smart technologies embedded with machine learning (ML) and presented in electronic formats to better inform homeowners and occupants about safety and maintenance issues, as well as recommend courses of remedial action. The demonstrated technologies include robotic sensing in confined areas, LiDAR scans of structural shape and deformation, moisture and gas sensing, water leak detection, network embedded ML, and augmented reality interfaces with multi-user teaming capabilities. The sensor information passes through a private local dynamic network to processors with neural network pattern recognition capabilities to abstract the information, which then feeds to humans through augmented reality and conventional smart device interfaces. This networked sensor system serves as a testbed and demonstrator for home maintenance technologies, for what can be termed Home Maintenance 4.0.

Detection of Gate Valve Leaks through the Analysis Fractal Characteristics of Acoustic Signal

This paper considers the possibility of using monofractal and multifractal analysis of acoustic signals to detect water leaks through gate valves. Detrended fluctuation analysis (DFA) and multifractal detrended fluctuation analysis (MF-DFA) were used. Experimental studies were conducted on a ½-inch nominal diameter wedge valve, which was fitted to a ¾-inch nominal diameter steel pipeline. The water leak was simulated by opening the valve. The resulting leakage rates for different valve opening conditions were 5.3, 10.5, 14, 16.8, and 20 L per minute (L/min). The Hurst exponent for acoustic signals in a hermetically sealed valve is at the same level as a deterministic signal, while the width of the multifractal spectrum closely matches that of a monofractal process. When a leak occurs, turbulent flow pulsations appear, and with small leak sizes, the acoustic signals become anticorrelated with a high degree of multifractality. As the leakage increases, the Hurst exponent also increases and the width of the multifractal spectrum decreases. The main contributor to the multifractal structure of leak signals is small, noise-like fluctuations. The analysis of acoustic signals using the DFA and MF-DFA methods enables determining the extent of water leakage through a non-sealed gate valve. The results of the experimental studies are in agreement with the numerical simulations. Using the Ansys Fluent software (v. 19.2), the frequencies of flow vortices at different positions of gate valve were calculated. The k-ω SST turbulence model was employed for calculations. The calculations were conducted in a transient formulation of the problem. It was found that as the leakage decreases, the areas with a higher turbulence eddy frequency increase. An increase in the frequency of turbulent fluctuations leads to enhanced energy dissipation. Some of the energy from ordered processes is converted into the energy of disordered processes.

Industrial-Based GSM Water Leakage Detection, Monitoring and Controlling System: A case of North Rift Valley Water Agency in Kenya

The current system for detecting and monitoring water leaks in Kenyan industries is manual and costly. Despite emerging new technological trends, many industries lack automated systems to detect, monitor, and control water leakage due to the high cost of maintenance and installation. This study's objective was to develop an automatic, remote, and real-time detection, monitoring, and, control system for water leaks. The system is made up of two nodes, one at the source and one at the destination or tap. The two nodes are made up of an ESP microcontroller, which is used to control all the connected components. The use of the ESP 32 microcontroller was efficient due to its ability to provide WI-FI. Aside from the solenoid valve, which was used to turn the water flow on or off in the event of leaks, the system also includes the FY-201 water flow sensor, which was used to gauge the amount of water flowing through the pipe. Water leakage is detected when the volume of water passing through the two sensors differs in terms of volume, indicating that a water leakage has just occurred. Thing-Board, an IoT-based platform used to monitor and visualize data from various devices connected, was used for real-time monitoring, visualization, and control. The developed system was tested with different water service providers, and the results showed that the system responds positively to water leakage parameters with capability of monitoring water leakages in a real time.

A Standalone Sensing and Actuation IoT Solution for Water Management, Leakage Detection, and Localization Problems

A Standalone Sensing and Actuation IoT Solution for Water Management, Leakage Detection, and Localization Problems

Automated extraction of tunnel leakage location and area from 3D laser scanning point clouds

Water leakage is one of the common tunnel disease problems. Fast and accurate detection is crucial for timely management of leakage disease. This paper introduces a method for automatically extracting the location and area of tunnel leakage from 3D laser scanning point clouds. The method involves three key steps: segmenting the point cloud data along the tunnel axis based on the width of the tube sheet, correcting the intensity of the point cloud using a linear intensity correction model, and binarizing the point cloud data to extract the leakage area and calculate its area. To evaluate the performance of the proposed methodology, water leakage detection software is prepared based on the method, and tests are carried out in the urban subway tunnels in the leakage disease-producing sections. The results show a detection accuracy of 92 % and an improvement in detection efficiency of 7–8 times. The method proposed can be used to quickly and accurately extract the location and area of water leakage from point cloud data.

Advanced acoustic leak detection in water distribution networks using integrated generative model

Water distribution networks (WDNs) experience significant water loss due to leaks, necessitating advanced water leak detection methods. However, machine learning-based acoustic method heavily relies on signal information and is limited by data scarcity and the limited diversity of available data. To address this challenge and enhance water leak detection in WDNs, this study proposes an LSTM-GAN approach. Acoustic signals are collected from WDNs to train the LSTM-GAN model, which generates synthetic leak signals to enhance the dataset. The validity of the generative method is evaluated through t-SNE and acoustic characteristics analysis. LSTM-based water leak detection models are established and compared using the original and the generated datasets to confirm the efficacy of generated samples in improving water leak detection performances. The capability of LSTM-GAN has been evaluated through different perspectives, including sensitivity analysis and model comparison. The results validate the quality and consistency of the generated acoustic signals under leak conditions. Besides, the optimal number of generated samples should be determined according to the requirements and characteristics of the leak detection task. Furthermore, the comparison between the proposed method and other acoustic generative methods demonstrates the superiority of LSTM-GAN-generated signals in enhancing the performance of leak detection models. The proposed generative method offers an innovative approach to facilitate machine learning-based leak detection models with limited data, thereby enhancing robustness.

Combining Cylindrical Voxel and Mask R-CNN for Automatic Detection of Water Leakages in Shield Tunnel Point Clouds

Water leakages can affect the safety and durability of shield tunnels, so rapid and accurate identification and diagnosis are urgently needed. However, current leakage detection methods are mostly based on mobile LiDAR data, making it challenging to detect leakage damage in both mobile and terrestrial LiDAR data simultaneously, and the detection results are not intuitive. Therefore, an integrated cylindrical voxel and Mask R-CNN method for water leakage inspection is presented in this paper. This method includes the following three steps: (1) a 3D cylindrical-voxel data organization structure is constructed to transform the tunnel point cloud from disordered to ordered and achieve the projection of a 3D point cloud to a 2D image; (2) automated leakage segmentation and localization is carried out via Mask R-CNN; (3) the segmentation results of water leakage are mapped back to the 3D point cloud based on a cylindrical-voxel structure of shield tunnel point cloud, achieving the expression of water leakage disease in 3D space. The proposed approach can efficiently detect water leakage and leakage not only in mobile laser point cloud data but also in ground laser point cloud data, especially in processing its curved parts. Additionally, it achieves the visualization of water leakage in shield tunnels in 3D space, making the water leakage results more intuitive. Experimental validation is conducted based on the MLS and TLS point cloud data collected in Nanjing and Suzhou, respectively. Compared with the current commonly used detection method, which combines cylindrical projection and Mask R-CNN, the proposed method can achieve water leakage detection and 3D visualization in different tunnel scenarios, and the accuracy of water leakage detection of the method in this paper has improved by nearly 10%.