Inspection and Understanding of Sewer Network Condition in Dindaeng District (Thailand)

Sewer pipelines are usually operated with little maintenance after installation. After years in service, sewer pipes get old and deteriorated.  The present research is the first study that attempts to inspect the condition of sewer pipelines in the Bangkok Metropolitan Area. A robot with Closed Circuit Television (CCTV) was used to capture videos and images of the interior of the sewer pipelines between two manholes. The system was operated by Utility Business Alliance (UBA) Company. The condition of the sewer pipelines was visually analyzed in line with the scoring and grading system of New Zealand’s pipe inspection manual. CCTV footage was received from UBA, and each meter of the sewer pipelines was visually inspected, scored, and graded. The study area included 22 randomly selected roads around Dindaeng District. For the structural condition, surface damage was found in all of the 89 inspected pipes.  Joint faulty and crack longitudinal were found in approximately 60% of the inspected sewer pipelines.  Infiltration present was found in 33% of the inspected pipes, while crack circumferential and crack multiple were minimal in the study area. For the service condition, obstruction temporary, debris silty, and debris greasy were found in 15%, 11%, and 4% of the inspected sewer pipelines, respectively. The grades of the structural condition were mainly between 4 and 5, indicating that most of the sewer pipelines in the area would need rehabilitation programs. The grades of the service condition were between excellent and good. This is the first time that such a program has been conducted in Thailand. The information of the condition of the pipelines and the inspection and characterization methods should be used as a reference for maintenance programs and future assessment programs. The adjustment or development of the inspection manual should be done in collaboration with local contacts

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research

Position and orientation correction for pipe profiling robots

Sewer pipelines are prevalent, important, valuable, unnoticed, and often in a state of disrepair. Pipeline inspection is essential for effective management of wastewater systems and is now mandated for many municipalities complying with the Governmental Accounting Standards Board Statement 34 and EPA regulations. Pipe inspection robots are routinely used to inspect underground pipelines for cracks, deformations, leaks, blockages and other anomalies to prevent catastrophic failure and to ensure cost effective maintenance and renewal. Most existing pipe inspection robots only collect video footage of pipe condition. Pipe profiling technology has recently been introduced to allow for measurement of the internal coordinate geometry of pipelines. Accurate radial measurements permit the calculation of several important pipe parameters which aid in the determination of pipe condition and prediction of time to failure. Significant research work has been completed in North America, Europe, Asia and Australia aimed at improving the accuracy and automation of the pipe inspection process. However, standard calibration, verification, reporting and analysis practices must be developed for pipe profilers if coordinate profiling data is to be effectively included in the long term management of pipeline assets. The objective of this research is to quantify the measurement error incurred by a pipe profiler\u27s misalignment with the pipe axis, present a new methodology to correct the measurement error, develop a prototype profiler to verify the equations derived herein, and to further the development of pipe profiler technology at the Trenchless Technology Center at Louisiana Tech University. Equations are derived for pipe ovality as a function of the robot\u27s position and orientation with respect to a pipe to demonstrate the magnitude of the error which is introduced by a robot\u27s misalignment with the pipe axis. A new technique is presented to estimate the position and orientation of a profiler using radial measurement devices at each of its ends. This technique is demonstrated by applying homogeneous coordinate transformations to simulated radial measurements based on mathematically generated data that would be obtained by incrementally rotating two parallel radial measuring devices in a perfectly cylindrical pipe. A prototype pipe profiling robot was developed to demonstrate the new position and orientation technique and to experimentally verify the measurement error caused by a robot\u27s misalignment with the pipe axis. This work improves the accuracy and automation of pipe profiling technology and makes a case for the development of industry standard calibration, verification, reporting and analysis practices

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

Parallel Platform-Based Robot for Operation in Active Water Pipes

This thesis presents a novel design for a pipe inspection robot. The main aim of the design has been to allow the robot to operate in a water pipe while it is still in service. Water pipes form a very crucial part of the infrastructure of the world we live in today. Despite their importance, water leakage is a major problem suffered by water companies worldwide, costing them billions of dollars every year. There are a wide variety of different techniques used for leak detection and localisation, but no one method is capable of accurately pinpointing the leak location and severity in all pipe conditions with minimal labour. A survey of existing pipe inspection robots showed that there have been many designs implemented that are capable of navigating the pipeline environment. However, none of these were capable of fully autonomous control in a live water pipe. It was concluded that an autonomous pipe inspection robot capable of working in active pipelines would be of great industrial benefit as it would be able to carry a wide range of sensors directly to the source of the leak with minimal, if any, human intervention. An inchworm robot prototype was constructed based on a Gough-Stewart parallel platform. The robot’s inverse kinematics equations were derived and a simulation model of the robot was constructed. These were verified using a motion capture suite, confirming that they are valid representations of the robot. The simulation was used to determine the robot’s movement limitations and minimum bend radius it could navigate. Several CFD simulations were carried out in order to estimate the maximum fluid force exerted on the robot. It was found that the robot’s design successfully minimised the fluid force such that off-the-shelf actuators had the capability to overcome it. The prototype was successfully tested in both a straight and bent pipe, demonstrating its ability to navigate a dry pipe environment. Overall, the robot prototype served as a successful proof of concept for a design of pipe inspection robot that would be capable of operating in active pipelines

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

Water pressure optimisation for leakage management using deep reinforcement learning

In this thesis, we introduce a novel approach to pressure management using deep reinforcement learning (DRL) algorithms. Exploiting DRL algorithms to optimise pressure management in water distribution networks (WDNs) provides a more computationally efficient and resilient method to reduce background and burst leakage. Using DRL to manage pressure has proven as a valuable method to reduce leakage and carbon emissions in two case studies based on a real and benchmark water network. A cohort of eight DRL algorithms of varying natures are implemented on a benchmark test network and real network model of varying sizes to prove their scalability. An investigation on their ability to reduce both background and burst leakage is conducted to highlight their abilities with regards to different leak sizes. The application of deep reinforcement learning algorithms to control leakage in WDNs builds on from two extensive reviews of leakage management and DRL applications in the urban water systems. Collating this literature pinpoints the novelty in applying deep reinforcement learning algorithms to control pressure in WDNs and provides context to the thesis. To develop DRL algorithms fit for WDN operations, a novel python-based environment is created that can communicate the hydraulic capabilities of EPANET to the DRL agent. This involved multiple design choices including action space and observation space selection as well as formulating a reward function suitable for the multiple objectives relating to leakage reduction. Regarding background leakage, the best performing DRL algorithm resulted in 65.2% reduction in leakage in the benchmark network. However, the investigation on the real water network provided by Northumbrian Water Living has proved the strong dependency between valve locations and pressure management hence resulting in a negligible background leakage reduction. The ability of the DRL algorithms to deal with uncertainty through randomised burst nodes was investigated in the second case study. DRL policies demonstrated resilience in comparison to the standard optimisation algorithms used (differential evolution, particle swarm optimisation, and nelder mead). The best performing DRL algorithm predicted a 58.46% leakage reduction and 5650kg of reduced CO2 emissions in the benchmark water network. On the other hand, the best DRL performance optimised the real water network by reducing the leakage by 5.79% and carbon emissions by 1999kg of CO2