Underground Water Pipes Research Articles

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.

Development of an Optimal RTK Calculation Program Using Genetic Algorithm

Rising levels of underground water and inappropriate pipe alignment during rainy weather have led to a rise in rainfall-derived infiltration and inflow (RDII), increasing incidences of separate sewer overflows (SSOs) and reducing the efficiency of sewage treatment systems. RTK analysis based on measured RDII data of the target area through computer modeling is essential when maintaining sewer pipes in order to address this RDII problem. In the Sanitary Sewer Overflow Analysis and Planning (SSOAP) program used for RTK analysis, calculating RTK using a trial and error method is a bit challenging. Accordingly, this study introduces a program to calculate the optimal RTK at the measurement point using a genetic algorithm. In ROP, accuracy was added to the RDII prediction mechanism by incorporating error rates based on time intervals into the evaluation factors of SSOAP, namely rainfall inflow rate and peak flow error rate. The error rate for the SSOAP program and ROP was validated as 2.66%.

Machine learning-assisted optimal schedule of underground water pipe inspection

There are over 2.2 million miles of underground water pipes serving the cities in the United States. Many are in poor conditions and deteriorate rapidly. Failures of these pipes could cause enormous financial losses to the customers and communities. Inspection provides crucial information for pipe condition assessment and maintenance plan; it, however, is very expensive for underground pipes due to accessibility issues. Therefore, water agencies commonly face the challenge to 1) decide whether it is worthwhile to schedule expensive water pipe inspections under financial constraints, and 2) if so, how to optimize the inspection schedule to maximize its value. This study leverages the physical model and data-based ML (ML) models for underground water pipe failure prediction to shed light on these two important questions for decision making. Analyses are firstly conducted to assess the value of water pipe inspection. Results by use of a physical-based failure model and Monte Carlo simulations indicate that by inspecting pipe’s condition, i.e., assessment of pipe’s erosion depth, the uncertainty of water pipe failure prediction can be narrowed down by 51%. For optimal inspection schedule, an artificial neural network (ANN) model, trained with historical inspection data, is evaluated for its performance in forecasting the future pipe failure probability. The results showed that a biased pipe failure prediction can occur under limited rounds of inspection. However, incorporating more rounds of inspection allows to predict the pipe failure conditions over its life cycle. From this, an optimal inspection plan can be proposed to achieve the maximum benefits of inspection in uncertainty reduction. A few salient results from the analyses include 1) the optimal schedule for inspection is not necessarily equal in the time interval, 2) by setting the goal of uncertainty reduction, an optimal inspection schedule can be obtained, where ML (ML) model augmented by continuously training with inspection data allows to reliably predict water pipe failure conditions over its life cycle. While this study focuses on underground pipe inspection, the general observations and methodology are applicable to optimize the inspection of other types of infrastructure as well.

Entrepreneurship and alignment work in the Swedish water and sanitation sector

Water and sewage (WS) systems are, like most grid based infrastructural systems, often centralised and hierarchical and the end user has almost no possibility to influence the technical standards, business models or system architecture. The preferred method for connecting new areas to the grid are underground water pipes and gravity flow for sewage. Thus, the WS system is “tightly coupled”. It is hard to change and conservative in its system culture, exhibiting a strong “momentum” or “path dependence”.In this article we investigate an unusual case in the development of WS-systems. As a rule, WS-systems, as most infrastructural systems, develop gradually through incremental innovations, and system owners/utilities traditionally build their systems “from the inside out”. In our case, we investigate a situation where the end users took the initiative to connect a residential area, Aspvik, part of the municipality of Värmdö, outside Stockholm, Sweden, to the municipal grid and thus expand the WS-system, not from the inside out, but from the outside in.Furthermore, we highlight another unusual feature: the role of a resident that acted as the “entrepreneur” in this process of WS-system expansion. The entrepreneur had unique trust building abilities in the local community, which the regime actor (the WS utility), could not match. Historically, inventor-entrepreneurs have been common, acting as “system builders” in the establishment phase of new infrastructural systems. However, entrepreneurs outside the regime are not common in the WS sector.Although atypical in mature WS systems in developed countries, these types of local initiatives or hybrid solutions are common in developing countries. In this article, we argue that there are lessons to be learnt from our case, when dealing with system expansion processes both inside and outside the Global North.

A proximate analysis of soil corrosivity to water pipelines in the Manohara Town Planning area of Kathmandu Valley using a probabilistic approach

The facts of early non-performance of iron-based underground water pipes of the Kathmandu Valley (Nepal) are mostly due to the corrosive nature of the surrounding soil factors. A comparatively high percentage of the damage to the water pipelines in Nepal is anticipated, compared with the data reported in the developed countries. For the reasons, the quantitative data analysis of six kinds of the corrosive soil factors (i.e., moisture-holding capacity, pH, electrochemical resistivity, redox potential, Cl−, and SO4 2− ions) of 15 specimens, sampled from the Manohara Town Planning (MTP) area, was carried out using a novel probabilistic model, which is based on the internationally accepted standards (i.e., ASTM, NACE, AASTHO) with some modifications. The corrosivity of each soil factor was categorized into four corrosive groups (CGs) based on the conventionally applied classification methods. The experimentally obtained data of the soil factors were then used for proximate analysis using a new non-deterministic (probabilistic) model to classify the corrosive sub-groups (CSGs) of the soil specimens. This model would be a meritorious method to assess the soil corrosion condition of any area of the country, where the engineers and technologists could be applied pertinent methods or materials for the buried-pipe works in the future.

Preliminary Investigation of an Approach to Improve Water Impermeability in Concrete with Externally Bonded FRP Systems

Good bond and water impermeability in fiber-reinforced polymer (FRP) bonded/coated systems are essential if the durability of FRP-rehabilitated concrete structures is to be ensured. In addition, water impermeability is required in some special FRP applications, such as the strengthening of underground water pipes. So far, there has been no method in the literature guaranteeing water impermeability in FRP strengthening works. This paper studies the feasibility of using a waterproof coating as the initial primer on cementitious materials before applying externally bonded FRP. In this preliminary investigation, by examining the two most important indicators (i.e., pull-off bond tests and water penetration tests), it was found that the use of an initial waterproof layer in the proposed FRP bonding system did not influence the pull-off bond strength but significantly improved the system's water impermeability. It is, therefore, suggested that an initial waterproof layer can be included in method statements for externally bonded FRP systems to upgrade the effectiveness and durability of FRP systems.

Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors

Underground water pipes deteriorate under the influence of various physical, mechanical, environmental, and social factors. Reliable pipe failure prediction is essential for a proactive management strategy of the water supply network (WSN), which is challenging for the conventional physics-based model. This study applied data-driven machine learning (ML) models to predict water pipe failures by leveraging the historical maintenance data heritage of a large water supply network. A multi-source data-aggregation framework was firstly established to integrate various contributing factors to underground pipe deterioration. The framework defined criteria for the integration of various data sources including the historical pipe break dataset, soil type dataset, topographical dataset, census dataset, and climate dataset. Based on the data, five ML algorithms, including LightGBM, Artificial Neural Network, Logistic Regression, K-Nearest Neighbors, and Support Vector Classification are developed for pipe failure prediction. LightGBM was found to achieve the best performance. The relative importance of major contributing factors on the water pipe failures was analyzed. Interestingly, the socioeconomic factors of a community are found to affect the probability of pipe failures. This study indicates that data-driven analysis that integrates the Machine Learning (ML) techniques and the proposed data integration framework has the potential to support reliable decision-making in WSN management.

Stereo Vision Combined With Laser Profiling for Mapping of Pipeline Internal Defects

Underground potable water pipes are essential infrastructure assets for any country. A significant proportion of those assets are deteriorating due to pipe corrosion which results in premature failure of pipes causing enormous disruptions to the public and loss to the economy. To address such adverse effects, the water utilities in Australia exploit advanced pipelining technologies with a motive of extending the service life of their pipe assets. However, the linings are prone to defects due to improper liner application and unfavorable environmental conditions during the liner curing phase. To monitor the imperfections of the pipe linings, in this article, we propose a mobile robotic sensing system that can scan, detect, locate and measure pipeline internal defects by generating three-dimensional RGB-Depth maps using stereo camera vision combined with infrared laser profiling unit. The system does not require complex calibration procedures and it utilizes orientation correction to provide accurate real-time RGB-D maps. The defects are identified and color mapped for easier visualization. The robotic sensing system was extensively tested in laboratory conditions followed by field deployments in buried water pipes in Sydney, Australia. The experimental results show that the RGB-D maps were generated with millimeter (mm) level accuracy with demonstrated liner defect quantification.

On Image Fusion of Ground Surface Vibration for Mapping and Locating Underground Pipeline Leakage: An Experimental Investigation.

This paper is concerned with imaging techniques for mapping and locating underground pipeline leakage. Ground surface vibrations induced by the propagating axisymmetric wave can be measured by an array of acoustic/vibration sensors, with the extraction of magnitude information used to determine the position of leak source. A method of connected graph traversal is incorporated into the vibroacoustic technique to obtain the spatial image with better accuracy compared to the conventional magnitude contour plot. Measurements are made on a dedicated cast iron water pipe by an array of seven triaxial geophones. The spectral characteristics of the propagation of leak noise signals from underground water pipes to the ground surface are reported. Furthermore, it is demonstrated that suspicious leakage areas can be readily identified by extracting and fusing the feature patterns at low frequencies where leak noise dominates. The results agree well with the real leakage position in the underground pipeline.

Analysis on underground water pipes multi-point leakage location method based on distributed optical fiber

基于拉曼散射测温原理，利用分布式光纤对UPVC（硬脂聚氯乙烯）和铸铁自来水管道进行模拟泄漏点定位实验。首先对采集到的原始Anti-Stokes光信号进行滤波平滑处理，再利用相关系数法对温度检测信号进行分类分析，识别自来水管有无泄漏发生，最后通过选择性阈值法识别出UVPC管以及铸铁管中的泄漏点位置。实验结果表明，该系统运行稳定且能够准确识别自来水管泄漏情况。选择性平均阈值法的使用，能够对200 m的埋地自来水管道准确地进行泄漏点的定位，定位误差为0.25 m～0.65 m。

Mechano-Magnetic Telemetry for Underground Water Infrastructure Monitoring

This study reports on the theory of operation, design principles, and results from laboratory and field tests of a magnetic telemetry system for communication with underground infrastructure sensors using rotating permanent magnets as the sources and compact magnetometers as the receivers. Many cities seek ways to monitor underground water pipes with centrally managed Internet of Things (IoT) systems. This requires the development of numerous reliable low-cost wireless sensors, such as moisture sensors and flow meters, which can transmit information from subterranean pipes to surface-mounted receivers. Traditional megahertz radio communication systems are often unable to penetrate through multiple feet of earthen and manmade materials and have impractically large energy requirements which preclude the use of long-life batteries, require complex (and expensive) built-in energy harvesting systems, or long leads that run antennas near to the surface. Low-power magnetic signaling systems do not suffer from this drawback: low-frequency electromagnetic waves readily penetrate through several feet of earth and water. Traditional magnetic telemetry systems that use energy-inefficient large induction coils and antennas as sources and receivers are not practical for underground IoT-type sensing applications. However, rotating a permanent magnet creates a completely reversing oscillating magnetic field. The recent proliferation of strong rare-earth permanent magnets and high-sensitivity magnetometers enables alternative magnetic telemetry system concepts with significantly more compact formats and lower energy consumption. The system used in this study represents a novel combination of megahertz radio and magnetic signaling techniques for the purposes of underground infrastructure monitoring. In this study, two subterranean infrastructure sensors exploit this phenomenon to transmit information to an aboveground radio-networked magnetometer receiver. A flow meter uses a propeller to directly rotate a diametrically magnetized neodymium magnet. A moisture sensor rotates a magnet with a low-power electric motor. Laboratory performance and field tests establish the capabilities of magnetic telemetry for IoT-linked leak-detection sensors. Remote datalogging with encryption demonstrates the viability of integrating sensors and surface receivers into a LoRa wireless IoT network.

Experiment and numerical simulation on seepage failure of sand caused by leakage of underground water pipe

Underground water pipe leakage accident is a serious threat to the city road and people's life safety, the internal mechanism is necessary to carry out related study. In this paper, numerical simulation technology and model test device was used for researching on the effect of different factors on the seepage failure of sand. The research shows that the evolutionary process of sand seepage failure induced by underground water pipe leakage can be divided into three stages: unfluidized stage stable cavity stage and fluidized stage. At the initial stage, the pressure distribution at different height reaches a peak with the increment of water pressure and flow rate. When the water pressure and flow rate is beyond the peak, cavity would occur at leakage hole. Under the action of seepage force, the cavity would dissipate at the surface of soil, and the distribution of fluidized bed pressure would maintain a stable state. Moreover, the smaller the leakage diameter, the greater the critical water pressure is. In a numerical test of a wedge angle of 62 degrees, F (upward drag force) is equal to the W (weight of wedge), which indicates the cavity reaches a force balance. The wedge with an angle of 62 degrees is significant to the sample model used in numerical simulation.

Monitoring underground water leakage pattern by ground penetrating radar (GPR) using 800 MHz antenna frequency

Water is the most treasure natural resources, however, a huge amount of water are lost during its distribution that leads to water leakage problem. The leaks meant the waste of money and created more economic loss to treat and fix the damaged pipe. Researchers and engineers have put tremendous attempts and effort, to solve the water leakage problem especially in water leakage of buried pipeline. An advanced technology of ground penetrating radar (GPR) has been established as one of the non-destructive testing (NDT) method to detect the underground water pipe leaking. This paper focuses on the ability of GPR in water utility field especially on detection of water leaks in the underground pipeline distribution. A series of laboratory experiments were carried out using 800-MHz antenna, where the performance of GPR on detecting underground pipeline and locating water leakage was investigated and validated. A prototype to recreate water-leaking system was constructed using a 4-inch PVC pipe. Different diameter of holes, i.e. ¼ inch, ½ inch, and ¾ inch, were drilled into the pipe to simulate the water leaking. The PVC pipe was buried at the depth of 60 cm into the test bed that was filled with dry sand. 15 litres of water was injected into the PVC pipe. The water leakage patterns in term of radargram data were gathered. The effectiveness of the GPR in locating the underground water leakage was ascertained, after the results were collected and verified.

Reliability or Sustainability

As a typical cyber-physical system (CPS), smart water distribution networks require monitoring of underground water pipes with high sample rates for precise data analysis and water network control. Due to poor underground wireless channel quality and long-range communication requirements, high transmission power is typically adopted to communicate high-speed sensor data streams, posing challenges for long-term sustainable monitoring. In this article, we develop the first sustainable water sensing system, exploiting energy harvesting opportunities from water flows. Our system does this by scheduling the transmission of a subset of the data streams, whereas other correlated streams are estimated using autoregressive models based on the sound-velocity propagation of pressure signals inside water networks. To compute the optimal scheduling policy, we formalize a stochastic optimization problem to maximize the estimation reliability while ensuring the system’s sustainable operation under dynamic conditions. We develop data transmission scheduling (DTS), an asymptotically optimal scheme, and FAST-DTS, a lightweight online algorithm that can adapt to arbitrary energy and correlation dynamics. Using more than 170 days of real data from our smart water system deployment and conducting in vitro experiments to our small-scale testbed, our evaluation demonstrates that Fast-DTS significantly outperforms three alternatives, considering data reliability, energy utilization, and sustainable operation.

Modeling and locating underground water pipe leak with microseismic data

Traditional pipeline leak locating methods require that geophones have to be placed on the pipe wall. While if the exact location of the pipeline is unknown, the leaks may not be identified accurately. To solve this problem, considering the characteristics of pipeline leak, a continuous random seismic source model is proposed and geological models are established. Based on the two dimensional (2D) viscoacoustic equations and the staggered grid finite-difference (FD) algorithm, the microseismic wave field generated by a leaking pipe is modeled. Cross-correlation analysis and the simulated annealing (SA) algorithm are employed to obtain the time difference and the leak location. Analysis and discussions of the effects of number of recorded traces, survey layout, and offset and trace interval on the accuracy of the estimated location are also conducted. Simulation and data field experiment results indicate that: (1) A continuous random source can realistically represent the leak microseismic wave field in a simulation using 2D viscoacoustic equations and staggered grid FD algorithm. (2) For the leak microseismic wave field, the cross-correlation method is effective for calculating time difference of the direct wave relative to the reference trace. However, outside the refraction blind zone, accuracy of the time difference is reduced by the effects of refracted wave. (3) The SA algorithm based upon time difference, helps to identify the leak location effectively, even in the presence of noise. Estimation of the horizontal distance is more accurate than that of the depth, and the locating errors increase with increasing number of traces and offset. Moreover, in the refraction blind zone, trace interval has almost no impact on the accuracy of the location estimate. And the symmetrical array provides a higher estimate accuracy than the asymmetrical array. (4) The acquisition method of time difference based on the microseismic theory and SA algorithm has a great potential for locating underground pipelines leak from an array located on the ground surface.

Pipe failure predictions in drinking water systems using satellite observations

Soil deformation is believed to play a crucial role in the onset of failures in the underground infrastructure. This article describes a method to generate a replacement-prioritisation map for underground drinking water pipe networks using ground movement data. A segment of the distribution network of a Dutch drinking water company was selected as the study area. Failure registration data comprising 868 failures registered over 40 months and geographical network data were obtained from the water utility. Ground movement was estimated using radar satellite data. Two types of analyses were performed: cell and pixel based. For the cell-based analysis, asbestos cement (AC) pipes exhibited the highest failure rates. Older AC pipes were also shown to fail more often, whereas failure rates for PVC were the lowest. For the pixel-based analysis, ground movement was demonstrated to play a role in the failure of all materials combined. Therefore, a replacement-prioritisation map for AC was generated which combined ground movement data and pipe-age data. This method can be a beneficial resource for network managers for maintenance and continuous monitoring.

Case study and back analysis of a residential building damaged by expansive soils

This paper presents a case study of a residential house damaged by expansive soils. The field investigation revealed that the damage was most likely caused by excessive lawn watering and leaks of sewer pipe and/or stormwater pipe, which resulted in non-uniform soil moisture conditions. Three-dimensional back analysis of this distressed structure indicated that stresses were most critical at a re-entrant corner and that steel reinforcing bars in the beam in this area had yielded. The results of the back analysis also indicated that a stronger footing was required to limit differential deflection to an acceptable level and reduce stress in the footing. The case study has clearly shown that a leaking underground water pipe and/or excessive watering of a garden could cause more severe distortion to a single storey masonry veneer house than could be expected from seasonal moisture change and the deeper moisture re-distribution caused by the imposition of the house on seasonally dry reactive soil. Moreover it has been demonstrated that it would be extremely costly to design a footing for extreme, or abnormal, moisture changes.

MCMC implementation for Bayesian hidden semi-Markov models with illustrative applications

Hidden Markov models (HMMs) are flexible, well-established models useful in a diverse range of applications. However, one potential limitation of such models lies in their inability to explicitly structure the holding times of each hidden state. Hidden semi-Markov models (HSMMs) are more useful in the latter respect as they incorporate additional temporal structure by explicit modelling of the holding times. However, HSMMs have generally received less attention in the literature, mainly due to their intensive computational requirements. Here a Bayesian implementation of HSMMs is presented. Recursive algorithms are proposed in conjunction with Metropolis-Hastings in such a way as to avoid sampling from the distribution of the hidden state sequence in the MCMC sampler. This provides a computationally tractable estimation framework for HSMMs avoiding the limitations associated with the conventional EM algorithm regarding model flexibility. Performance of the proposed implementation is demonstrated through simulation experiments as well as an illustrative application relating to recurrent failures in a network of underground water pipes where random effects are also included into the HSMM to allow for pipe heterogeneity.

Imaging the Molecular Structure of Polyethylene Blends with Broadband Coherent Raman Microscopy.

Polyethylene (PE) has been widely used in a myriad of consumer products and critical infrastructure products such as underground gas and water pipes. These products are often made of blends of multiple types of PE with different molecular architectures. Although the long-term performance of these products is largely dictated by their local molecular structure, it has been studied mostly by indirect and bulk-averaging methods such as calorimetry and neutron scattering due to lack of chemical contrast for conventional imaging techniques. We demonstrate that broadband coherent anti-Stokes Raman scattering (CARS) microscopy can acquire images of the chemical composition and molecular orientation of a miscible semicrystalline PE blend with two different molecular architectures. We discuss the detailed crystal structure observed at different length scales and new insights it provides into polymer crystal morphology.

On the prediction of underground water pipe failures: zero inflation and pipe-specific effects

The prediction of pipe failures in water distribution systems is an essential planning tool for water companies. Previous methods focus on the prediction of either future failure numbers or aspects of pipe condition. However, most of these only predict at the level of large pipe groups (of similar characteristics) and often cannot provide uncertainty bounds. Here, a new statistical method is developed to predict the probability of failure at the single pipe level. The method extends the Non-Homogeneous Poisson Process (NHPP) in two ways: firstly, it incorporates pipe-specific random effects to account for unmeasured information on the factors affecting the pipe failures. Secondly, the method explicitly accounts for zero inflation, that is the possibility that more zero failures occur than expected from a simple Poisson assumption. This zero-inflated NHPP (ZINHPP) model was applied to two real-life datasets, one from North America and one from New Zealand. The results clearly demonstrate improved prediction capability, especially in the New Zealand data, which contain a much larger percentage of pipes with zero failures.