Pipe Leakage Research Articles

Numerical analysis of the large-scale water supply network accident consequences on the engineering and geological conditions of a heritage site

Recent accidents in water supply networks have the negative impact on the state of the historical and architectural heritage of the Kyiv-Pechersk Lavra, which has been formed over many centuries and is a UNESCO World Heritage Site. We have analysed the water supply system emergency situation on the territory of the Metropolitan Garden that occurred in October 2022 during the Russian military aggression. It caused surface sinkholes, increased groundwater levels, and significant destruction of a historical underground – the Metropolitan Cellar. The study was carried out using numerical analysis implemented in Plaxis 2D. To resolve the uncertainties of the accident, several options for developing the emergency situation were considered, taking into account the number of probable pipe leakages, their size, etc. Accident parameterization was performed with a leakage volume of 1600.0 m3/day, considering damage to the water supply network in two locations. The volume of the watered soil mass was 7.0-8.5 thousand m3. We evaluated the state of destruction of the southern and southeastern branches of the Metropolitan Cellar. Engineering measures for strengthening the section of the Monastery walls with buttress elements, increasing the width of the foundations by means of additional concrete and piling are studied. The numerical calculations were verified using the results of geophysical surveys. Comparison of analytical calculations, geophysical surveys and field surveys showed that parts of the underground structure were completely destroyed. Their restoration is possible only by modern methods through reconstruction, that will lead to a loss of authenticity, which is unacceptable for historical structures. To take preventive actions for the protection of monuments, it is necessary to conduct continuous monitoring.

Analysis of Regional Characteristics of Climate Change Factors Affecting Water Distribution Pipe Leakage

Understanding the factors behind urban water leakage is crucial for developing a sustainable climate and protecting civil infrastructure. Water leaks not only waste essential resources but also increase urban vulnerabilities to climate-induced disasters. This study investigates the teleconnection between leakage incidents and climate change indices to establish predictive insight for water management. It focuses on climate phenomena such as El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), which significantly influence global climate dynamics, affecting temperature and precipitation in South Korea. Using Pearson correlation analysis and Granger causality tests, this research examines climate indices and leakage data across South Korea’s inland regions from 2009 to 2022. The results indicate that ENSO indices exhibit a lead time of 6 to 30 months, with significant correlations in coastal areas, particularly Chungnam (west coast) and Gyeongnam (east coast). Inland regions such as Gimcheon and Chuncheon also showed notable correlations influenced by topographical factors. The findings highlight the importance of integrating climate teleconnection indices into risk management strategies. This approach allows for targeted monitoring and predictive modeling, enabling proactive responses to water leakage risks and contributing to sustainable urban development.

Modeling and advanced Ground Penetrating Radar techniques for evaluating pipe leakage and pavement distress

This study provides a comprehensive analysis of Ground Penetrating Radar (GPR) data processing techniques for detecting water leakage in urban pavements. By examining GPR images characterized by continuous and discontinuous high-amplitude reflections, the research identifies clear indicators of water saturation caused by leaking underground pipes. Computer modeling of GPR profiles enables effective comparisons with real data and helps in calibrating key GPR attributes such as energy, amplitude variance, and RMS amplitude. These attributes allow for the precise delineation of leakage zones in both synthetic and real GPR datasets collected in the Amazon region of Brazil. The Reflector Tilt Method (RTM) significantly enhances GPR data analysis, especially for detecting subsurface water leaks. RTM utilizes changes in the relative dielectric constant and electromagnetic wave velocity induced by water saturation in unconsolidated, horizontally stratified sand layers. By analyzing the tilt of reflections in GPR profiles, RTM differentiates between dry and saturated zones, providing insights into the geometry and extent of water infiltration. Notably, this study demonstrates how increased subsurface water saturation causes underlying reflectors to tilt. Using the tilt angle, we propose a novel technique to quantify the increase in the dielectric constant due to water saturation. This technique could also be adapted to detect leaks involving other fluids, as long as there is a strong contrast between the dielectric constant and the background. The integration of RTM improves the creation of accurate three-dimensional models of affected areas, enhancing leak detection and aiding in understanding water movement dynamics within the subsurface. The study underscores the importance of combining GPR attributes with RTM to generate detailed 3D models of leakage zones, which are essential for estimating the volume of infiltrated areas. Observations indicate significant water saturation near leaking pipes at shallow depths, with saturation levels decreasing rapidly as depth increases. By identifying 2D geophysical signatures of water leaks in public distribution networks, the research highlights the effectiveness of non-invasive GPR methods in mitigating risks such as soil erosion and surface collapse. This approach demonstrates the utility of GPR attributes and RTM as essential tools for assessing urban pavement integrity, enabling early detection of leaks and precise mapping of affected zones. The findings contribute significantly to the efficient and sustainable management of urban infrastructure, enhancing maintenance strategies and reducing potential hazards associated with undetected fluid leaks.

Impact-Echo Testing Methods for Determining the Defect of Cured-In-Place Pipe

Non-destructive testing of cured-in-place pipe (CIPP) ensures that the liner (CIPP) has been successfully installed and that the pipe will function reliably, the Acoustic methods are used to detect cracks or leaks in pipes as the Impact-Echo (IE) method. This study aims to design a device to carry the tools inside the pipe. The received signals were analyzed to identify delamination, cavities behind CIPP, and overall pipe health.

Feasibility of Using Hypothetical Fractal Structures to Determine Water Outflow Zones After a Pipe Failure

Failures of water supply pipes are undesirable events with a random nature, yet they are an inevitable part of the operation of water infrastructure. Therefore, ongoing research is being conducted to develop methods for minimising their effects or securing underground infrastructure. One of the methods of limiting the effects of the suffosion phenomenon is determination of the water outflow zone, within which water will possibly flow to the soil surface after a pipe leak. The aim of this paper was to assess hypothetical structures created by outflowing water in terms of their potential use in determining the water outflow zone on the soil surface after a water pipe failure. Based on the laboratory test results, the Monte Carlo method was applied to generate the hypothetical population of points representing the places of water outflow. Three parameters characterising hypothetical structures were analysed: fractal dimension, length of a section, and the product of above parameters. The conducted research showed that it is possible to build a reliable hypothetical structure that allows for estimating the water outflow zone radius, knowledge of which would facilitate sustainable management of the water supply network by water utilities by enabling the estimation of the water outflow zone radius in practice.

On the Significance of Parameter Uncertainties for Prediction of Leak Noise Wave Speed in Buried Pipes

Abstract The modern world is facing the challenging issue of water wastage due to leaks, which is causing severe economic, environmental and social impacts. Consequently, the inspection and maintenance of buried water pipes is crucial and there is still a lack of investigations towards the uncertain parameters affecting the wave speed associated with the predominantly fluid-borne wave s=1, the main carrier of leak noise. This study investigates the effects of uncertainties present in the pipe and soil parameters which are affecting the speed of propagation of the leak noise wave. To achieve this, a sensitivity analysis is performed using Monte Carlo simulations and Sobol’ indices. Uncertainties are commonly associated with the material and geometrical properties of the pipe along with the surrounding soil characteristics. However, the significance of these parameters varies depending on the type of soil in which the water pipe is buried. In clay soil, the soil-related parameter plays a crucial role compared to sandy soil and this is verified through some experimental work carried out in two water pipe systems with very different properties, one in the UK and the other one in Brazil. This research is of fundamental importance for determining the most critical parameters affecting the leak noise wave, allowing to evaluate and integrate uncertainty information into decision-making of current technologies, such as loggers and leak noise correlators, aiming enhanced detection and location of water leakage in buried plastic water pipes.

Gas Pipe Leakage Inspection Robot with Real Time Monitoring System

In this Review paper the study focusses on Pipe inspection robots,robots are advanced devices engineered to traverse the interiors of pipelines, assessing conditions and identifying issues like leaks, corrosion, or blockages in industries such as oil and gas, water supply, and sewage. Equipped with high-resolution cameras, sensors, and diagnostic tools, these robots offer detailed, real-time data to support preventive maintenance and reduce manual inspection challenges. Recent developments focus on improving robot agility, compactness, and durability, enabling them to handle varied pipe diameters and complex pathways. Enhanced with wireless connectivity and artificial intelligence, some models can now perform autonomous navigation and defect detection, significantly boosting efficiency and safety. This review examines current innovations, operational hurdles, and future advancements in pipe inspection robotics, with a particular emphasis on smarter navigation systems and sensor technology to enhance inspection accuracy and dependability.

Hydraulics-based pipe leak detection revisited and experimentally verified

ABSTRACT Leak detection is an integral component of pipelines that often fails due to one or the other reason and leads to several issues of serious concern requiring immediate attention. This paper revisits a technique based on hydraulic principles of flow of water through pipes and verifies it experimentally for its applicability using reasonably large set of data derived experimentally for various pipes of different materials and diameters, pipe flows, and leak discharges/locations. The study finds that the available hydraulic equation for leak localization is valid only for long pipes, not for short pipes experiencing significant impact of leak-generated negative waves on inlet and outlet pressures, too sensitive to localization. The proposed new (more elaborate) equation not only works well for both short and long pipes but also enables a consistent description of the effects of pipe materials, diameters, pipe flows, and leakages and their sensitivity to localization. In an attempt to further support the study results, the acoustics-based measurements were also analyzed for describing the level of leakage in pipes with increasing decibel magnitude, and the results were found to be not only cogent but also consistent in describing the impacts of pipe materials/sizes and leak locations on noise level.

Cross-System Fault Propagation Mechanism in Heat-Electrical Integrated Energy Systems: Insights From Pipe Leakage

Cross-System Fault Propagation Mechanism in Heat-Electrical Integrated Energy Systems: Insights From Pipe Leakage

From manual to UAV-based inspection: Efficient detection of levee seepage hazards driven by thermal infrared image and deep learning

Levee failures are caused mainly by river water seepage erosion inside the levee, manifesting as slope leakage and foundation piping phenomena. To address the urgent need for levee safety monitoring during flood seasons and extreme rainfall events, unmanned aerial vehicles (UAVs) equipped with thermal infrared imagers can quickly detect leakage and piping hazards based on temperature differences between damaged areas and their surroundings. In this study, we collected 5995 UAV thermal infrared images of leakage and piping on levees in four floodplains under various weather, time, and surface coverage conditions to evaluate the applicability of combining thermal infrared imaging with a deep learning model in complex natural environments. We categorized levee hazards into water piping, ground piping, and slope leakage, and developed a Mask R-CNN segmentation model. The results revealed that thermal infrared levee inspection was affected by vegetation occlusion and subtle temperature differences caused by continuous rainstorms and water body depth. The Mask R-CNN demonstrated strong generalizability to hazards with significant variability in shape, size, and temperature difference, making it suitable for detecting evolving and expanding damaged area. The mean average precision, recall, and precision of the Mask R-CNN were 0.977, 0.982, and 0.897, respectively, and the detection time was 0.015 s per image. Moreover, Eigenvector-based class activation mapping (Eigen-CAM) was used to visualize the decision basis and failure modes of the Mask R-CNN to improve interpretability. Seepage damage is a progressive process, so timely hazard identification can provide valuable time for levee repair.

Quality improvement using lean six sigma in air conditioning products

Air Conditioners (AC) are essential electronic products for residential and industrial buildings in tropical countries or during warm weather in general. The penetration ratio of electronic Air Conditioners (AC) has been increasing every year. Data collection and processing were conducted directly by the author through observation, measurement, and calculation on the heat exchanger production of Air Conditioners (AC). In the production cost of Air Conditioners (AC), the heat exchanger accounts for the second highest cost after the compressor making up 19% of the total cost. The high cost is caused by pipe leaks, with a percentage of 73.7%. This research aims to analyze the improvement of Air Conditioner (AC) production quality using the Six Sigma method. The defect rate in the heat exchanger, evaluated through the Define, Measure, Analyze, Improve, Control (DMAIC) method originating from Six Sigma, was found to be 1,144.8 defects per million opportunities. Further analysis was conducted with the Failure Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA) methods. This research was conducted at XYZ company by conducting interviews and observations for 3 months. FMEA and FTA analysis resulted in the top two failure risks based on the Risk Priority Number (RPN): flaring diameter too big and unstable brazing skill. In the manual brazing process, it was found that work by operators with level A certification produced 9% below standard outcomes. An improvement was obtained by changing the punch flaring size to 8.5mm and conducting skill certification and training for brazing operators. The results of the proposed improvement implementation led to a reduction in the DPMO value to 403.35, equivalent to a decrease of 64.7%. The defect ratio in production decreased by 38.1%, exceeding the previous target of 26.2%, equivalent to $31.96/month/unit. The results of this study are horizontal, allowing the implementation of improvements made in the heat exchanger production process to be expanded to other cooling electronic production processes.

Research on the generation and modulation of active pressure wave for pipelines leak diagnosis

In this paper, a novel device that uses compressed air to generate pressure waves in the liquid pipeline is proposed. Experimental investigations are carried out to explore the modulation method of the pressure wave generated and the application potential of the device in leak diagnosis. The consequences show that the device can generate safe, stable and controllable pressure waves. The characteristics of produced pressure wave under different excitation frequencies and inflation pressure are investigated with the combination of experiments and theory analysis. The parameters that affect the excitation behaviors of the device are pointed out theoretically. Finally, the performance of the device under continuous excitation and single excitation is tested in the leaking pipe. Through the extraction and analysis of the signal features in the frequency domain, the leakage can be diagnosed and distinguished under both excitation methods.

Analysis of the Suboptimal Performance of the Steering Gear on MT. Narpatisuta

The steering gear is one of the auxiliary machineries on board a ship that is used to move the ship's rudder, allowing the ship to turn to the right or left. This machinery utilizes an electro-hydraulic system, which combines electric and hydraulic technologies to control the ship's steering direction. The research focuses on analyzing the suboptimal performance of the steering gear on the MT. Narpatisuta. The objectives of the research are to identify the factors causing the suboptimal performance of the steering gear, the impacts resulting from this suboptimal performance, and the efforts made to address the suboptimal performance of the steering gear on the MT. Narpatisuta. The research method used is a qualitative descriptive method, with data collection techniques in the form of observation, interviews, documentation and literature study. The data analysis technique uses the SHEL method. Test the validity of the data using triangulation. The research results show that suboptimal performance of the steering gear is caused by hydraulic pipe leaks, deteriorating quality of hydraulic oil, and damage to seal and o-ring components, which lead to reduced steering gear performance, slower rudder movement, and can disrupt ship operations, endangering crew safety and the ship's cargo. Efforts that can be made to remedy this include routine inspection and maintenance of the steering gear and its components. Thus, this research emphasizes the need for routine inspection and maintenance to prevent these problems which must ensure maximum steering gear function, maintain safety and ship operational efficiency.

Neural-network-based sound source localization from unmanned aerial vehicles

As a mobile platform, unmanned aerial vehicles (UAV) can carry optics and acoustics sensors recording visual and audio information from ground. Over the past decade, UAVs are widely used in different applications, e.g., search and rescue, pipe leakage detection, etc. Compared with visual information, audio information is challenging to capture from UAVs due to the strong ego-noise generated by the rotating motors, propellers on the UAV as well as the motion of the UAV, which result in a low signal to noise ratio (SNR) of the received acoustics signals. With the contaminated signals, the performance of sound source localization algorithms, e.g., conventional beamforming, is severely degraded. To improve the quality of sound source localization result, different sound source enhancement algorithms have been proposed by researchers. In this paper, simulation experiments were conducted to study the potential of apply convolutional neural networks (CNN) to improve the beamforming result. It was found that with CNN, the estimation of a single point sound source location and source strength can be improved. The model's localization accuracy and source strength estimation accuracy were evaluated respectively.

Evaluation of Electrochemical Properties and Life Prediction of Sensor Wire in Leak Detection Systems of Underground Heating Pipelines

In this study, we investigated the electrochemical properties and lifespan of the NiCr (NiCr 8020) sensor wire of a resistance leaking detection (LD) system to detect pipe corrosion and leakage in an actual district heating (DH) system. The temperature and applied stress of the sensor wire during the actual operation of the resistance LD system of the DH system were derived through simulations and calculations. The anodic dissolution of the sensor wire was accelerated with the increased temperature and the applied current. The corrosion type changed from localized corrosion, such as pitting, to uniform corrosion. The applied stress caused ductile fracture of the thinned sensor wire by anodic dissolution. In conclusion, we confirmed that in the resistance LD system of a DH system, where current and stress are applied at high temperatures, the sensor wire becomes thin due to the anodic dissolution and subsequent ductile fracture. In addition, the lifespan of the sensor wire was derived according to the resistance level measured in the resistance LD system of the DH system. Our findings contribute to preventing failure and improving the reliability of the resistance LD systems of DH systems.

Predicting the spread of contamination in water distribution networks laid on sloping terrains

Contamination in domestic and drinking water during the conveyance process is one of the biggest health risks of all time. This is a very common hazard that is expected in the areas where water, sewer, and drainage pipes are laid in trenches near each other. The extent of the contamination spread in the case of intrusion through a pipe leak for water distribution networks (WDNs) laid on sloping terrain is not known. This article deals with the simulation and hydraulic analysis of organic contaminant intrusion in WDNs with significant consideration of the slope of the laid pipes. The source of organic contamination is considered to be the nearby leaking sewer water. The effects of sloping terrain on contaminant spread in the pipe network are studied in detail by injecting contaminant concentrations at eight different critical locations in the network. The results of contamination spread after a particular time at all nodes are compiled, and by using statistical techniques, a relation between the contaminant spread and pipe slope is proposed. The presented model is validated by comparing the actual values of the contaminant in water samples obtained at the site with the calculated ones, and it shows that the values deviate within the range of ±2% only, which is considered a good match. The research proves to be beneficial for the management of water distribution through pipe networks against contaminants to maintaining water quality and public health.

An Improved Convolutional Neural Network for Pipe Leakage Identification Based on Acoustic Emission

Oil and gas pipelines are the lifelines of the energy market, but due to long-term use and environmental factors, these pipelines are prone to corrosion and leaks. Offshore oil and gas pipeline leaks, in particular, can lead to severe consequences such as platform fires and explosions. Therefore, it is crucial to accurately and swiftly identify oil and gas leaks on offshore platforms. This is of significant importance for improving early warning systems, enhancing maintenance efficiency, and reducing economic losses. Currently, the efficiency of identifying leaks in offshore platform pipelines still needs improvement. To address this, the present study first established an experimental platform to simulate pipeline leaks in a marine environment. Laboratory leakage signal data were collected, and on-site noise data were gathered from the “Liwan 3-1” offshore oil and gas platform. By integrating leakage signals with on-site noise data, this study aimed to closely mimic real-world application scenarios. Subsequently, several neural network-based leakage identification methods were applied to the integrated dataset, including a probabilistic neural network (PNN) combined with time-domain feature extraction, a Backpropagation Neural Network (BPNN) optimized with simulated annealing and particle swarm optimization, and a Long Short-Term Memory Network (LSTM) combined with Mel-Frequency Cepstral Coefficients (MFCC). Corresponding models were constructed, and the effectiveness of leak detection was validated using test sets. Additionally, this paper proposes an improved convolutional neural network (CNN) leakage detection technology named SART-1DCNN. This technology optimizes the network architecture by introducing attention mechanisms, transformer modules, residual blocks, and combining them with Dropout and optimization algorithms, which significantly enhances data recognition accuracy. It achieves a high accuracy rate of 99.44% on the dataset. This work is capable of detecting pipeline leaks with high accuracy.

Experimental Study of Sinkhole Propagation Induced by a Leaking Pipe Using Fibre Bragg Grating Sensors.

Sinkhole formation caused by leaking pipes in karst soluble rocks is a significant concern, leading to infrastructure damage and safety risks. In this paper, an experiment was conducted to investigate sinkhole formation in dense sand induced by a leaking pipe. Fibre Bragg grating (FBG) sensors were used to record the strain. A balloon was gradually deflated within a bed of wet silica sand to create an underground cavity. Eighteen FBG sensors, with a wavelength range between 1550 nm and 1560 nm, were embedded horizontally and vertically in the physical model at different levels to monitor deformation at various locations. A leaking pipe was installed to induce the collapse of the formed arch above the cavity. The strain measurements suggested the following four phases in the sinkhole formation process: (1) cavity formation, (2) progressive weathering and erosion, (3) catastrophic collapse, and (4) subsequent equilibrium conditions. The results showed differences in the strain signatures and distributions between the horizontal and vertical measurements. During the critical phase of the sinkhole collapse, the horizontal measurements primarily showed tension, while the vertical measurements indicated compression. This investigation demonstrates the effectiveness of FBGs as advanced monitoring tools for sinkhole precursor identification. The study also suggests using FBGs in geotechnical monitoring applications to improve the understanding and mitigation of sinkholes and related geohazards.

A Simplified Failure Assessment to Identify Crack Growth Behavior in the Gas Pipeline by Post-Hydrostatic Pressure

Hydrostatic pressure tests on pipelines have been found to be an inadequate means of critical integrity management due to the frequency of false negatives, which result from the inability of these tests to detect crack growth. It can be argued that focusing on pipe leakage does not guarantee future operability. A study presents a failure assessment methodology based on the failure assessment diagram (FAD), which aims to predict crack growth during hydrotesting. The calibrated pipe spool is validated by the application of data from hydrostatic tests and analytical techniques to ascertain the potential growth in circumferential surface cracks. A variety of factors, including pressure, material grades, flaw dimensions, and elliptical flaw angles, were examined in an effort to assess cracks. The results demonstrate that there is no pipeline leakage and minimal trapped air. Despite its location within the plastic zone, with a normalized pressure index of ≤1, the pipeline is deemed to be within acceptable limits according to the criteria established by the FAD. The assessment point was found to be predominantly influenced by the toughness ratio of the material grade. The crack propagated in the opposite direction with a maximum length a/c= 0.125 and a crack depth a/t= 0.2, which limited the toughness ratio. The load ratio indicates uniformity in elliptical angle flaw results. In this simplified failure assessment, the parameter describing the flaw size, which exhibits a strong correlation with the toughness ratio, plays a pivotal role. Further research and recommendations are also proposed.

LSTM-Autoencoder Based Detection of Time-Series Noise Signals for Water Supply and Sewer Pipe Leakages

The efficient management of urban water distribution networks is crucial for public health and urban development. One of the major challenges is the quick and accurate detection of leaks, which can lead to water loss, infrastructure damage, and environmental hazards. Many existing leak detection methods are ineffective, especially in complex and aging pipeline networks. If these limitations are not overcome, it can result in a chain of infrastructure failures, exacerbating damage, increasing repair costs, and causing water shortages and public health risks. The leak issue is further complicated by increasing urban water demand, climate change, and population growth. Therefore, there is an urgent need for intelligent systems that can overcome the limitations of traditional methodologies and leverage sophisticated data analysis and machine learning technologies. In this study, we propose a reliable and advanced method for detecting leaks in water pipes using a framework based on Long Short-Term Memory (LSTM) networks combined with autoencoders. The framework is designed to manage the temporal dimension of time-series data and is enhanced with ensemble learning techniques, making it sensitive to subtle signals indicating leaks while robustly dealing with noise signals. Through the integration of signal processing and pattern recognition, the machine learning-based model addresses the leak detection problem, providing an intelligent system that enhances environmental protection and resource management. The proposed approach greatly enhances the accuracy and precision of leak detection, making essential contributions in the field and offering promising prospects for the future of sustainable water management strategies.