Water and Wastewater Pipe Nondestructive Evaluation and Health Monitoring: A Review

Civil infrastructures such as bridges, buildings, and pipelines ensure society's economic and industrial prosperity. Specifically, pipe networks assure the transportation of primary commodities such as water, oil, and natural gas. The quantitative and early detection of defects in pipes is critical in order to avoid severe consequences. As a result of high-profile accidents and economic downturn, research and development in the area of pipeline inspection has focused mainly on gas and oil pipelines. Due to the low cost of water, the development of nondestructive inspection (NDI) and structural health monitoring (SHM) technologies for fresh water mains and sewers has received the least attention. Moreover, the technical challenges associated with the practical deployment of monitoring system demand synergistic interaction across several disciplines, which may limit the transition from laboratory to real structures. This paper presents an overview of the most used NDI/SHM technologies for freshwater pipes and sewers. The challenges that said infrastructures pose with respect to oil and natural gas pipeline networks will be discussed. Finally, the methodologies that can be translated into SHM approaches are highlighted

A comprehensive review of acoustic methods for locating underground pipelines

Underground pipelines are vital means of transporting fluid resources like water, oil and gas. The process of locating buried pipelines of interest is an essential prerequisite for pipeline maintenance and repair. Acoustic pipe localization methods, as effective trenchless detection techniques, have been implemented in locating underground utilities and shown to be very promising in plastic pipeline localization. This paper presents a comprehensive review of current acoustic methods and recent advances in the localization of buried pipelines. Investigations are conducted from multiple perspectives including the wave propagation mechanism in buried pipe systems, the principles behind each method along with advantages and limitations, representative acoustic locators in commercial markets, the condition of buried pipes, as well as selection of preferred methods for locating pipelines based on the applicability of existing localization techniques. In addition, the key features of each method are summarized and suggestions for future work are proposed. Acoustic methods for locating underground pipelines have proven to be useful and effective supplements to existing localization techniques. It has been highlighted that the ability of acoustic methods to locate non-metallic objects should be of particular practical value. While this paper focuses on a specific application associated with pipeline localization, many acoustic methods are feasible across a wide range of underground infrastructures

Review of Physical Based Monitoring Techniques for Condition Assessment of Corrosion in Reinforced Concrete

Monitoring the condition of steel corrosion in reinforced concrete (RC) is imperative for structural durability. In the past decades, many electrochemistry based techniques have been developed for monitoring steel corrosion. However, these electrochemistry techniques can only assess steel corrosion through monitoring the surrounding concrete medium. As alternative tools, some physical based techniques have been proposed for accurate condition assessment of steel corrosion through direct measurements on embedded steels. In this paper, some physical based monitoring techniques developed in the last decade for condition assessment of steel corrosion in RC are reviewed. In particular, techniques based on ultrasonic guided wave (UGW) and Fiber Bragg grating (FBG) are emphasized. UGW based technique is first reviewed, including important characters of UGW, corrosion monitoring mechanism and feature extraction, monitoring corrosion induced deboning, pitting, interface roughness, and influence factors. Subsequently, FBG for monitoring corrosion in RC is reviewed. The studies and application of the FBG based corrosion sensor developed by the authors are presented. Other physical techniques for monitoring corrosion in RC are also introduced. Finally, the challenges and future trends in the development of physical based monitoring techniques for condition assessment of steel corrosion in RC are put forward

Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark

Perhaps surprisingly sewerage infrastructure is one of the most costly infrastructures in modern society. Sewer pipes are manually inspected to determine whether the pipes are defective. However, this process is limited by the number of qualified inspectors and the time it takes to inspect a pipe. Automatization of this process is therefore of high interest. So far, the success of computer vision approaches for sewer defect classification has been limited when compared to the success in other fields mainly due to the lack of public datasets. To this end, in this work we present a large novel and publicly available multi-label classification dataset for image-based sewer defect classification called Sewer-ML. The Sewer-ML dataset consists of 1.3 million images annotated by professional sewer inspectors from three different utility companies across nine years. Together with the dataset, we also present a benchmark algorithm and a novel metric for assessing performance. The benchmark algorithm is a result of evaluating 12 state-of-the-art algorithms, six from the sewer defect classification domain and six from the multi-label classification domain, and combining the best performing algorithms. The novel metric is a class-importance weighted F2 score, $\text{F}2_{\text{CIW}}$, reflecting the economic impact of each class, used together with the normal pipe F1 score, $\text{F}1_{\text{Normal}}$. The benchmark algorithm achieves an $\text{F}2_{\text{CIW}}$ score of 55.11% and $\text{F}1_{\text{Normal}}$ score of 90.94%, leaving ample room for improvement on the Sewer-ML dataset. The code, models, and dataset are available at the project page https://vap.aau.dk/sewer-ml/Comment: CVPR 2021. Project webpage: https://vap.aau.dk/sewer-ml

Acoustic Monitoring for Leaks in Water Distribution Networks

Water distribution networks (WDNs) are complex systems that are subjected to stresses due to a number of hydraulic and environmental loads. Small leaks can run continuously for extended periods, sometimes indefinitely, undetected due to their minimal impact on the global system characteristics. As a result, system leaks remain an unavoidable reality and water loss estimates range from 10\%-25\% between treatment and delivery. This is a significant economic loss due to non-revenue water and a waste of valuable natural resource. Leaks produce perceptible changes in the sound and vibration fields in their vicinity and this aspect as been exploited in various techniques to detect leaks today. For example, the vibrations caused on the pipe wall in metal pipes, or acoustic energy in the vicinity of the leak, have all been exploited to develop inspection tools. However, most techniques in use today suffer from the following: (i) they are primarily inspection techniques (not monitoring) and often involve an expert user to interpret inspection data; (ii) they employ intrusive procedures to gain access into the WDN and, (iii) their algorithms remain closed and publicly available blind benchmark tests have shown that the detection rates are quite low. The main objective of this thesis is to address each of the aforementioned three problems existing in current methods. First, a technology conducive to long-term monitoring will be developed, which can be deployed year-around in live WDN. Secondly, this technology will be developed around existing access locations in a WDN, specifically from fire hydrant locations. To make this technology conducive to operate in cold climates such as Canada, the technology will be deployed from dry-barrel hydrants. Finally, the technology will be tested with a range of powerful machine learning algorithms, some new and some well-proven, and results published in the open scientific literature. In terms of the technology itself, unlike a majority of technologies that rely on accelerometer or pressure data, this technology relies on the measurement of the acoustic (sound) field within the water column. The problem of leak detection and localization is addressed through a technique called linear prediction (LP). Extensively used in speech processing, LP is shown in this work to be effective in capturing the composite spectrum effects of radiation, pipe system, and leak-induced excitation of the pipe system, with and without leaks, and thus has the potential to be an effective tool to detect leaks. The relatively simple mathematical formulation of LP lends itself well to online implementation in long-term monitoring applications and hence motivates an in-depth investigation. For comparison purposes, model-free methods including a powerful signal processing technique and a technique from machine learning are employed. In terms of leak detection, three data-driven anomaly detection approaches are employed and the LP method is explored for leak localization as well. Tests were conducted on several laboratory test beds, with increasing levels of complexity and in a live WDN in the city of Guelph, Ontario, Canada. Results form this study show that the LP method developed in this thesis provides a unified framework for both leak detection and localization when used in conjunction with semi-supervised anomaly detection algorithms. A novel two-part localization approach is developed which utilizes LP pre-processed data, in tandem with the traditional cross-correlation approach. Results of the field study show that the presented method is able to perform both leak-detection and localization using relatively short time signal lengths. This is advantageous in continuous monitoring situations as this minimizes the data transmission requirements, the latter being one of the main impediments to full-scale implementation and deployment of leak-detection technology

Pressure-based leakage characterisation of bulk pipelines

Water losses in distribution systems are a huge problem internationally and also in South Africa where more than a third of the water entering the water supply networks is lost through pipe leaks. With water demand increasing due to population growth and urbanisation, water resources are under greater stress and water supply failures are becoming more common. A great deal of work has been done over the past two decades on managing water losses in distribution systems. The Water Loss Task Force of the International Water Association (IWA) played a leading role in this effort, with the “IWA water balance” now widely used as a basis in municipal water loss programs. One of the areas that have received relatively little attention is leakage on bulk pipeline systems. Bulk pipelines connect water treatment plants to bulk reservoirs and distribute water from reservoirs to different towns or water supply zones. Bulk pipes may be operated using pumps or gravity, and generally do not supply consumers directly. It is difficult to determine what the water losses in a bulk pipeline are, as the high flow rates make it impractical or prohibitively expensive to measure flow rates at both ends of these pipelines. Cheaper solutions, such as clamp-on ultrasonic flow meters or reservoir drop tests, are prone to problems and do not have the required accuracy. Due to the lack of reliable and effective methods, water losses on bulk pipes are often assumed to be 2 or 3 %. However, these losses may, in fact, be much greater, and due to the large flow of water transported by bulk pipelines, even small fractions of losses represent large volumes of water. The aim of this project was to develop a method for identifying the size and type of leak present in real bulk water pipelines with minimal disturbance to the operation of the infrastructure. This was achieved by developing a mobile device called the pipe condition assessment equipment (PCAE), which uses pressure testing in combination with the latest models on the behaviour of leaks areas with pressure to assess the condition of the bulk pipeline. To verify the efficacy of the PCAE, the device was first tested on three uPVC pipes with known leakage characteristics in the laboratory (a 12mm round hole, 100mm by 1mm circumferential crack and a 100mm by 1mm longitudinal crack). As expected, the round hole had very small head-area slopes which are negligible, whilst the circumferential crack showed a negative head area slope and the longitudinal crack portrayed a large positive head-area slope. These results were consistent with previous laboratory experiments that investigated the behaviour of round holes and longitudinal and circumferential cracks. Bulk water suppliers and municipalities were then approached to take part in the study. Several bulk pipelines were tested using the PCAE. The results of the field test are discussed in terms of the pre-testing procedures to prepare for the tests, their repeatability and the effectiveness of the device to detect, quantify and characterise leakage on the pipeline. For pipelines with undetectable leakage, a non-intrusive technique that uses a dynamic pressure drop signature from an isolated pipe, to detect and quantify undetectable leakage, was developed. The leakage characteristics of the isolated pipe were estimated from the pressure vs time data. In summary, if the pressure remained constant the pipe was without a leak. If the pressure dropped, a novel mathematical model was fitted to the pressure vs time curve, using the known pipe properties, to determine the characteristics of the leak or leaks present in the pipe. Overall, the PCAE was capable of assessing the extent of leakage on a range of pipe materials, diameters and lengths. It was found that out of the eleven bulk pipelines tested in this study, three could not be tested due to dysfunctional isolation valves and failed connection points. The other eight pipelines that were successfully tested were found to be leaking. The effective initial leak areas for the tested pipelines ranged from 4.88mm2 to 137.66mm2 , whilst the effective head-area slope ranged from 0.0032 mm2 /m to 3.14 mm2 /m and the N1 leakage exponents were found to range from 0.56 up to 1.09. Finally, since there are no well-founded performance indicators for bulk systems, this study also described the findings from analyses of several potential performance indicators using the data from the bulk pipelines tested using the PCAE. The challenges in comparing water losses of different bulk pipelines are highlighted. Based on this, it was found that because every bulk pipeline has its unique characteristic regarding structural parameters such as diameter, pipe material, type of couplings, and operating pressure, the preferred performance indicator for assessing water losses in bulk systems mainly depends on the purpose of the analysis

Towards practical pressure-based leakage characterisation of water distribution pipes with a novel pipe condition assessment device

Leakage detection and management have been proposed as effective ways of mitigating and managing water losses in an age where water scarcity has become prevalent. To this end, several methods have been developed and suggested with different benefits and drawbacks The presently available leakage detection methods, however, fail to identify and characterise the leakage while simultaneously assessing the condition of the water distribution network (WDN). This function is imperative for understanding and addressing leakage. WDN assessments are also important as knowledge of the network parameters helps in reducing water losses through planned infrastructure maintenance programmes. A pipe condition assessment device (PCAD) was thus developed which can detect, characterise leakage and assess the condition of the WDN. However, the efficacy and reliability of this device had not yet been established. In this study, the device was used to characterise leakage and assess system conditions in water networks. Initially, laboratory tests on six known leak types were conducted on a standardised laboratory setup. The leakage characteristics of these pipes were found through regression analysis. The results from the tests established that to 95% level of confidence; the standardised setup can produce repeatable and comparable results to previous studies. The accuracy of the PCAD instrumentation was verified and the device calibrated, the same pipes were then tested on the standardised setup using the PCAD. An overlap of the results from the laboratory experiments and the PCAD revealed that to 95% level of confidence, the device could adequately characterise leakage in pipes. A low variance of less than 4% of the mean parameter, across all tests conducted using the PCAD, informed that the results obtained through using the PCAD are repeatable and reliable. Field tests in the Kensington DMA were done and revealed the limitations of the device, such as its inability to characterise leakage in pipe sections that cannot be successfully isolated. However, in pipelines that were successfully isolated, the PCAD was able to detect and identify leakage characteristics in water networks and aid in conducting maintenance runs. Consequently, this study contributed to the body of knowledge by statistically establishing that the PCAD could adequately, and reliability characterise leakage in real water distribution networks

Automated Sewer Inspection Analysis and Condition Assessment

Underground infrastructure serves an essential need for the society. Huge number of facilities is dedicated to facilitate the well-being’s needs. Sewer infrastructure, one of the facilities, plays a major role in maintaining healthier environment. Its main duty is to transfer sewage material to treatment plants or any designated disposal area. Therefore, providing well performing sewer systems is essential to avoid any breakdown. Nevertheless, sewer pipelines’ condition in North America is deteriorating. In fact, studies have shown that 30% of municipal infrastructure in Canada is in either fair or very poor condition. As a result, there is a significant requirement for inspection and rehabilitation. Many municipalities utilize Closed Circuit Television (CCTV) inspection technique in inspecting sewer pipelines. However, this technique suffers from significant subjective and imprecise conclusions. Hence, studying, analyzing and applying different sewer inspection technologies and designing a condition assessment model are necessary to reduce subjectivity and errors and produce accurate and reliable results. This research aims to develop an automated tool to quantify: deformation, settled deposits, infiltration and surface damage sewer defects. The automated approach is dependent upon using image processing techniques and several models to analyze output data from 2D laser profiler, sonar and electroscan. Other than using ASTM F1216 formula, the research suggests applying the roundness factor in quantifying the deformation defect. The research develops a condition assessment model, based on the aforementioned defects, to arrive to an aggregated index suggesting the condition of sewer pipelines. Multi Attribute Utility Theory (MAUT) approach is used for each defect. The research also suggests a methodology to evaluate the surface damage defect of sewer pipelines for reinforced concrete, vitrified clay and ductile iron sewer pipeline materials. An interface, using MATLAB, was developed to implement the designed quantification algorithms and the MAUT model on real case studies. After implementing and validating the two deformation quantification methods, the Mean Absolute Error (MAE) utilizing the ASTM F1216 was 4.27%, while the MAE using the roundness factor was 4.83%. The maximum difference percentage was found to be 40.06%; however, the minimum difference percentage was 0.59%. The average difference percentage for all the cases was calculated as 16.67%. Later, the MAUT model was validated with actual case studies. Three rounding types (rounding to nearest number, rounding up and down) were tested to change the aggregated index, containing decimals, to a whole number. Mean Absolute Error (MAE) was utilized to compare the rounding types. In all case studies, rounding up type produced the lowest MAE values. When rounding up the computed index in case study 1, the MAE for Concordia Sewer Protocol (CSP), Water Research Centre (WRc) and New Zealand were 0.33, 0.33 and 0.42, respectively. This research shall encourage subject matters to utilize technologies, other than or beside CCTV, to conclude sound results. The developed automated user interface shall reduce inaccuracy and subjectivity through the application of robust image processing algorithms. After extending this research in including several sewer’s components and defects, the condition assessment model shall aid asset managers to allocate their maintenance and rehabilitation budgets

Active Acoustics for Monitoring Unreported Leaks in Water Distribution Networks

Water distribution networks (WDNs) are critical infrastructure elements conveying water through thousands of kilometers of pipes. Pipes - one of the most critical elements in such systems - are subjected to various structural and environmental degradation mechanisms, eventually leading to leaks and breaks. Timely detection and localization of such leaks and bursts is crucial to managing the loss of this valuable resource, maintaining hydraulic capacity, and mitigating serious health risks which can potentially arise from such events. Much of the literature on leak detection has focused on passive methods; recording and analyzing acoustic signatures produced by leak(s) from passive piezo acoustic or pressure devices. Passive acoustic methods have received disproportionate attention both in terms of research as well as practical implementation for leak (or, bursts) detection and localization. Despite their popularity, passive methods have shown not to be reliable in detecting and localizing small leaks in full-scale systems, primarily due to acoustic signal attenuation and poor signal-to-noise ratios, especially in plastic materials. In this dissertation, an active method is explored, which uses an acoustic source to generate acoustic signatures inside a pipe network. A combination of active source and hydrophone receivers is demonstrated in this thesis as a viable method for monitoring leaks in water distribution pipes. The dissertation presents experimental results from two layouts of pipes, one a simple straight section and another a more complex network with tees and bends, with an acoustic source at one end, and hydrophones at strategic locations along the pipe. For leak detection, the measured reflected and transmitted energy using hydrophone receivers is used to determine the presence of a leak. To this effect, new leak indicators such as power reflection and transmission coefficients, power spectral density, reflected spectral density, and transmission loss are developed. Experimental results show that the method developed in this thesis can detect leaks robustly and has significant potential for use in pressurized water distribution systems. This thesis also presents a new framework for active method-based localization. Starting with a simple straight section for a proof of concept study and moving to lab-based WDNs, several methods are explored that simultaneously detect and locate a leak. The primary difficulty in detecting and estimating the location of a leak is overcome through a statistical treatment of time delays associated with multiple acoustic paths in a reverberant environment and estimated using two approaches: (i) classical signal decomposition technique (Prony's / matrix pencil method (MPM)) and (ii) a clustering pre-processing approach called mean-shift clustering. The former works on the cross-correlation of acoustic data recorded at two locations, while the latter operates on acoustic sensor data from a single location. Both methods are tested and validated using experimental data obtained from a laboratory testbed and are found to detect and localize leaks in plastic pipes effectively. Finally, time delay estimates obtained from Prony's / MPM are used in conjunction with the multilateration (MLAT) technique and extended Kalman filter (EKF) for localization in more complex WDNs. This study shows that the proposed active technique can detect and reliably localize leaks and has the potential to be applied to complex field-scale WDNs

Novel communication system for buried water pipe monitoring using acoustic signal propagation along the pipe

This research presents the design and development of a novel wireless underground communication system for buried water pipe monitoring, using acoustic signal propagation along the pipe. One of the main challenges for wireless underground communication in buried water pipe monitoring is the limitation of reliable data communication range, between an underground transmitter and receiver, to less than 3 metres using radio signal propagation. In this work, an alternative means of enabling data communication within an underground soil environment was investigated by using the water pipe wall as an acoustic communication medium. With acoustic transducers carefully selected from an abundance of commercially available options, a digital communication transmitter was developed alongside a separate digital communication receiver according to the low cost (tens of pounds at most), low power supply requirement (in the order of 1 Watt-hour) and miniature (centimetre scale) size of a wireless communication node. Following the transmitter and receiver design, the developed system was tested in the laboratory along an above ground medium density polyethylene (MDPE) pipe as well as in the field along buried steel and MDPE pipes with reliable digital communication (i.e., 0% bit error rate) successfully achieved at 3.0 and 5.6 m along the buried steel and MDPE pipes respectively with these pipes buried in well or poorly graded SAND (SW or SP). To analyse acoustic signal attenuation along the water pipes (a key requirement for predicting maximum data transmission range within the proposed communication system), three separate approaches were employed, i.e., analytical, numerical, and experimental (laboratory and field) approaches. While the analytical model was based on fundamental acoustic propagation equations, the numerical model was developed using Abaqus software to simulate acoustic propagation along the pipe; and the experimental approach directly measured acoustic signal attenuation along the pipes in the laboratory and field experiments. The analytical model and experimental results were used to validate the acoustic attenuation predictions of the numerical model. For the above ground MDPE pipe, the numerical model and laboratory experiments predicted a maximum data communication range of 18-42 m while for the buried MDPE and steel pipes, the field measurements predicted a maximum data communication range of 14-17 m. The results for the buried water pipes are particularly important as they show the possibility of using low frequency (< 1 kHz) acoustic signal propagation along a buried water pipe for achieving reliable wireless underground communication in soil