Segmentation of buried concrete pipe images

The enormity of the problem of deteriorating pipeline infrastructure is widely apparent. Since a complete rebuilding of the piping system is not financially realistic, municipal and utility operators require the ability to monitor the condition of buried pipes. Thus, reliable pipeline assessment and management tools are necessary to develop long term cost effective maintenance, repair, and rehabilitation programs. In this paper a simple, robust and efficient image segmentation algorithm for the automated analysis of scanned underground pipe images is presented. The algorithm consists of image pre-processing followed by a sequence of morphological operations to accurately segment pipe cracks, holes, joints, laterals, and collapsed surfaces, a crucial step in the classification of defects in underground pipes. The proposed approach can be completely automated and has been tested on five hundred scanned images of buried concrete sewer pipes from major cities in North America.     

Computer Vision Techniques for Automatic Structural Assessment of Underground Pipes

Pipeline surface defects such as cracks cause major problems for asset managers, particularly when the pipe is buried under the ground. The manual inspection of surface defects in the underground pipes has a number of drawbacks, including subjectivity, varying standards, and high costs. An automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer asset managers an opportunity to significantly improve quality and reduce costs. This article presents a system for the application of computer vision techniques to the automatic assessment of the structural condition of underground pipes. The algorithm consists of image preprocessing, a sequence of morphological operations to accurately extract pipe joints and laterals (where smaller pipe is connected to main bigger pipe), and statistical filters for detection of surface cracks in the pipeline network. The proposed approach can be completely automated and has been tested on over 1,000 scanned images of underground pipes from major cities in North America.     

Automated defect detection tool for closed circuit television (cctv) inspected sewer pipelines

In sewer networks, the economic effects and costs that result from a pipeline failure are rising sharply. As a result, there is huge demand for inspection and rehabilitation of sewer pipelines. In addition to being inaccurate, current practices of sewer pipelines inspection are time consuming and may not keep up with the deterioration rates of the pipelines. This papers presents the development of an automated tool to detect some defects such as: cracks, deformation, settled deposits and joint displacement in sewer pipelines. The automated approach is dependent upon using image-processing techniques and several mathematical formulas to analyze output data from Closed Circuit Television (CCTV) camera images. The automated tool was able to detect cracks, displaced joints, ovality and settled deposits in pipelines using CCTV camera inspection output footage using two different datasets. To examine the performance of the proposed detection methodology, confusion matrices were constructed, in which true positives for crack, settled deposits and displaced joints were 74%, 53% and 65%. As for the ovality, all defects in the images were detected successfully. Although these values could indicate low performance, however the proposed methodology could be improved if additional images were used. Given that one inspection session can result in hundreds of CCTV camera footage, introducing an automated tool would help yield faster results. Additionally, given the subjective nature of evaluating the severity of defects, it would result in more systematic outputs since the current method rely heavily on the operator's experience.     

Automated defect detection for sewer pipeline inspection and condition assessment

To regularly and proactively assess conditions of sewer infrastructure systems to ensure their structural integrity and continuity of services, it is critical to advance the state of automated pipeline inspection and condition assessment. Currently, a critical issue is to address realistic defect detection that deals with real sewer inspection data. This paper presents the findings of a research project that seeks to enable automated detection of defects in sewer pipelines from inspection videos and images. The need for and the challenges of automated defect detection in sewer infrastructure condition monitoring are presented. Based on a general detection and recognition model established in this paper, a change detection based approach which is tailored to solve the challenges in this sewer pipeline domain is described and illustrated through case study.     

Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques

Sanitary sewer systems are designed to collect and transport sanitary wastewater and stormwater. Pipe inspection is important in identifying both the type and location of pipe defects to maintain the normal sewer operations. Closed-circuit television (CCTV) has been commonly utilized for sewer pipe inspection. Currently, interpretation of the CCTV images is mostly conducted manually to identify the defect type and location, which is time-consuming, labor-intensive and inaccurate. Conventional computer vision techniques are explored for automated interpretation of CCTV images, but such process requires large amount of image pre-processing and the design of complex feature extractor for certain cases. In this study, an automated approach is developed for detecting sewer pipe defects based on a deep learning technique namely faster region-based convolutional neural network (faster R-CNN). The detection model is trained using 3000 images collected from CCTV inspection videos of sewer pipes. After training, the model is evaluated in terms of detection accuracy and computation cost using mean average precision (mAP), missing rate, detection speed and training time. The proposed approach is demonstrated to be applicable for detecting sewer pipe defects accurately with high accuracy and fast speed. In addition, a new model is constructed and several hyper-parameters are adjusted to study the influential factors of the proposed approach. The experiment results demonstrate that dataset size, initialization network type and training mode, and network hyper-parameters have influence on model performance. Specifically, the increase of dataset size and convolutional layers can improve the model accuracy. The adjustment of hyper-parameters such as filter dimensions or stride values contributes to higher detection accuracy, achieving an mAP of 83%. The study lays the foundation for applying deep learning techniques in sewer pipe defect detection as well as addressing similar issues for construction and facility management.     

Evaluation of uncertainties in sewer condition assessment

Closed Circuit Television Inspection is used since decades as industry standard for sewer system inspection and structural performance evaluation. In current practice, inspection data are helpful to support asset management decisions. However, the quality and uncertainty of sewer condition assessment is rarely questioned. This article presents a methodology to determine the probability to underestimate, overestimate or accurately estimate the real condition of a pipe using visual inspection. The approach is based on the analysis of double inspections of the same sewer pipes and has been tested using the extensive data-set of the city of Braunschweig in Germany. Results indicate that the probability to inspect correctly a pipe in poor condition is close to 80%. The probability to overestimate the condition of a pipe in bad condition (false negative) is 20% whereas the probability to underestimate the condition of a pipe in good condition (false positive) is 15%. Finally, sewer condition evaluation can be used to assess the general condition of the network with an excellent accuracy probably because the respective effects of false positive and false negative are buffered.     

Automated detection of cracks in buried concrete pipe images

The detection of cracks in concrete infrastructure is a problem of great interest. In particular, the detection of cracks in buried pipes is a crucial step in assessing the degree of pipe deterioration for municipal and utility operators. The key challenge is that whereas joints and laterals have a predictable appearance, the randomness and irregularity of cracks make them difficult to model. Our previous work has led to a segmented pipe image (with holes, joints, and laterals eliminated) obtained by a morphological approach. This paper presents the development of a statistical filter for the detection of cracks in the pipes. We propose a two-step approach. The first step is local and is used to extract crack features from the buried pipe images; we present two such detectors as well as a method for fusing them. The second step is global and defines the cracks among the segment candidates by processes of cleaning and linking. The influences of the parameters on crack detection are studied and results are presented for various pipe images.     

Automated detection of surface defects in water and sewer pipes

Automation is gaining momentum in industry, particularly in rehabilitation and inspection works of underground infrastructure facilities. This paper describes a model for automating inspection and identification of surface defects in underground water and sewer pipes. The paper describes the current efforts in identification of surface defects in underground water and sewer mains, and presents an automated system designed to assist infrastructure engineers in diagnosing defects in this class of pipe networks. It describes the general architecture of the system and its basic components, and focuses primarily on four modules designed for automating image acquisition, image processing, features extraction and classification of defects.     

Risk-based asset management: automated structural reliability assessment of geographically distributed pipeline networks for gas and water in the Netherlands

Pipeline networks for gas and water are the lifelines of our society. Most pipelines are buried, which obstructs direct monitoring of the pipes, and introduces the need for other techniques to provide network operators with information on the structural reliability of their network. One of the threats related to pipelines, possibly jeopardising the integrity of the network, is deformation of the subsurface surrounding the pipes, which imposes stress in the pipes. This paper describes the development of a fully computerised and automated model to calculate the probability of failure caused by subsurface deformation of pipes over time in the Netherlands. The objective is to provide failure probability information to network operators assisting the decision-making process regarding preventive measures, such as timely replacement. It will result in a reduction of costs associated with pipe failure, and lead to increased safety (e.g. explosion hazard) and certainty of delivery. The methodology used to develop the model is denoted as the STOOP system-of-systems. First results of the model are presented for a case study and show that different data sources and different models can be combined into a system of systems capable of performing predictive assessments.     

Prediction models for sewer infrastructure utilizing rule-based simulation

Management of infrastructure projects is becoming increasingly challenging due to inherent uncertainties. The most effeective way to deal with uncertainty is to collect supplementary information and knowledge. When expensive or infeasible, quantification of uncertainty may be performed using analytical or simulation techniques. The City of Edmonton, Canada has approximately 4600 km of sewer pipes in the combined, sanitary, and storm sewer local systems with uncertainty issues related to deterioration. The City has taken a proactive approach with respect to sewer rehabilitation, as it is more cost-effeective to repair a defective pipe prior to failure rather than after a collapse. This article demonstrates an approach for predicting the condition of a sewer pipe and the related cost of rehabilitation, given the limited data. Three models are described in this article that are developed to assist the City of Edmonton to effeectively plan maintenance expenditure. Each model uses a combination of rule-based simulation and probability analysis to assist in the planning of future expenditures for sewer maintenance, thereby producing an invaluable planning tool.     

跨断层地下管线振动台模型试验研究Ⅱ:试验成果分析

针对跨断层地下管线进行振动台模型试验研究。试验中将钢管埋设在一个盛装砂土、可以模拟走滑断层错动作用的模型箱中,研究地下管线在承受断层错动时应变的分布规律和管周动土压力变化规律,并考察地下管线与断层的夹角以及管内水体的影响。试验结果表明:在跨断层地下管线中,管-土系统本身的动力效应的影响较小,可以忽略不计,而管内液体可能有较大的影响,不可忽略;在无法避开断层区域的情况下,地下管线最好与断层垂直;管线的最大应变发生在断层附近一定距离的位置;管-土动力相互作用及其变化规律对管道响应影响很大。     

A Survival Analysis Model for Sewer Pipe Structural Deterioration

Abstract: The structural state of sewer systems is often quantified using condition classes. The classes are based on the severity of structural defects observed on individual pipes within the system. Here, a survival analysis model was developed to predict the overall structural state of a sewer network based on camera inspection data from a sample of pipes in the system. The convolution product was used to define the survival functions for cumulative staying times in each condition class. An original calibration procedure for the sewer deterioration model was developed to overcome the censored nature of data (left censored and right censored) available for the calibration of sewer deterioration models. The exponential and Weibull functions were used to represent the distribution of waiting times in each deterioration state. Cross‐validation tests showed that the Weibull function led to greater uncertainty than the exponential function for the simulated proportion of pipes that are in a deteriorated state. Using various sample sizes for model calibration, these cross‐validation tests also showed that the model's results are robust to smaller calibration sample sizes. This confirms the model's potential for predicting the overall state of deterioration of a sewer network when only a small proportion of the pipes have been inspected.     

跨逆断层埋地管线受力特性的管土效应分析

断层错动引起的地表永久性大变形是导致埋地管线破坏的重要因素之一,埋地管线的断层反应特性受到土介质和管线材料等因素的影响,本文以逆断层位错作用下的无缝直埋地管线为研究对象,基于ABAQUS有限元分析软件,建立埋地管线-土介质的三维有限元数值分析模型,利用非线性接触描述管-土接触状态,考虑初始地应力条件的影响,对跨逆断层埋地管线的受力特性进行数值模拟分析,依据计算结果主要分析了位错量、管线材料、管-土相互作用、土介质、管径等参数对埋地管线断层反应的影响效应,并得到:管-土介质接触效应是管线断层反应分析中不可忽视的影响因素,粘聚力较小、硬度较低的中硬土介质中的埋地管线抗断层性能较优等有益结论。     

Continuation ratio model for the performance behavior of wastewater collection networks

Canada’s aged wastewater infrastructure is failing. New financial and environmental regulatory requirements demand municipalities to estimate operating and capital expenditures for running the systems into the future, and to develop plans for financial sustainability while protecting public health and the environment. Presently, wastewater pipelines’ deterioration is not well understood and realistic deterioration models need to be developed.This paper presents a new ordinal regression model for the deterioration of wastewater pipelines based on continuation ratio logits. The model is presented using the generalized linear model formulation, and takes into account the ordinal nature of the dependent variable and the interaction effects between explanatory variables. The model provides estimates of conditional probabilities for a pipeline to advance beyond a particular internal condition grade – to worse condition – depending on pipe material and age. The model development and validation procedure is demonstrated using high quality condition assessment data for reinforced concrete (RC) and vitrified clay (VC) pipes from the City of Niagara Falls wastewater collection system.The new model is found to represent the RC and VC pipes’ degradation behavior for in-service pipes up to 110 years of age at the City of Niagara Falls wastewater collection system. RC pipes’ deterioration is found to be age dependent while VC pipes’ deterioration is not age dependent. The VC pipe finding is contrary to other deterioration model studies that indicate that the type of pipe material is not significant and that the deterioration of VC pipes is age dependent. The analysis shows, for example, that the predicted conditional probability for RC pipes to advance beyond internal condition grade 3 is estimated to be 60% at 40 years of age and it increases to 90% at 80 years. Similarly, there is a 60% chance of advancing beyond grade 4 to collapsed/collapse imminent condition at 80 years of age for RC pipes. VC pipes are found to have an indefinite service life if installed without structural damage. However, VC pipes exhibited relatively higher conditional probabilities than RC pipes for advancing to worse internal condition grades for pipes up to 65 years of age. Poor installation practices that resulted in pipe defects, such as open/displaced joints and defective connections are deemed to be the factors that resulted in VC pipe deterioration.The findings from the continuation ratio model can be used for risk-based policy development for maintenance management of wastewater collection systems. The proposed model can help in devising appropriate intervention plans and optimum network maintenance management strategies based on pipelines’ age, material type, and internal condition grades. These predictions are critical if realistic wastewater networks’ future maintenance and operation budgets are to be developed over the life of asset and to meet new regulatory reporting requirements. Further research is required to validate the proposed model in other networks and to determine if the method can be used to model the deterioration of pipe materials other than RC and VC.     

Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine

Condition assessment of municipal sewer pipes using closed circuit television (CCTV) inspections is known to be time consuming, costly, and prone to errors primarily due to operator fatigue or novicity. Automated detection of defects can provide a valuable tool for ensuring the quality, accuracy, and consistency of condition data, while reducing the time and cost of the inspection process. This paper presents an efficient pattern recognition algorithm to support automated detection and classification of pipe defects in images obtained from conventional CCTV inspection videos. The algorithm employs the histograms of oriented gradients (HOG) and support vector machine (SVM) to identify pipe defects. The algorithm involves two main steps: (1) image segmentation to extract suspicious regions of interest (ROI) that represent candidate defect areas; and (2) classification of the ROI using SVM classifier that was trained using sets of HOG features extracted from positive and negative examples of the defect. Proposed algorithm is applied to the problem of detecting tree root intrusions. The performance of linear and radial basis function SVM classifiers evaluated. The algorithm was tested on a set of actual CCTV videos obtained from the cities of Regina and Calgary in Canada. Experimental results demonstrated the viability and robustness of the algorithm.     

A method to consider replaced pipes in modeling of sewer pipe deterioration

Statistical models that predict the deterioration of sewer pipes are useful for planning financial resources required for sewer renewal. Usually, data that are available to calibrate these models solely concern pipes that are still in place, leading to underestimated deterioration rates. A new method is proposed to consider possible past replacement of pipes in the statistical modeling of their deterioration. The proposed method considers the aging of pipes, simulated with a Cox model, and their probability to be replaced separately. Application to a synthetic sewer network, for which it was assumed that information regarding all pipe replacements over the lifetime of the network was available, showed that the proposed method allows for improved predictions of the sewer deterioration model, when compared to predictions of a model calibrated without considering the information about replaced pipes.     

Water pipeline failure detection using distributed relative pressure and temperature measurements and anomaly detection algorithms

This paper presents the validation of a novel leak detection method for water distribution pipelines, although it could be applied to any buried pressurized fluid flow pipe. The detection method is based on a relative pressure sensor attached non-invasively to the outside of the pipe combined with temperature difference measurements between the pipe wall and the soil. Moreover, this paper proposes an anomaly detection algorithm, originally developed for monitoring website traffic data, which differentiates a ‘leak’ event from ‘normal’ pressure change events. It is compared to two more commonly used methods based on a fixed threshold and a moving average. The validation of the new system in a field trial over a 6-month period showed that all the known leaks were identified with 98.45% accuracy, with the anomaly detection algorithm performing best, making this system a real contender for leak detection in pipes.     

A qualitative analysis of early defects present in PVC-U sewers but not observed in rigid pipes

The objective of the study was to analyze early defects in PVC-U sewer pipes that are not observed in rigid pipes. Field investigations were conducted on newly laid PVC-U sewer pipes in more than a dozen cities in Poland using CCTV equipment and a special device for measuring deflections. The analysis focused on qualitative data concerning defects observed in PVC-U sewers. Three types of defects, i.e. dents, deflections and buckling, were thoroughly analyzed to determine whether trenchless rehabilitation of such pipes is possible and to improve the existing sewer deterioration models. The major conclusions concern the types, sizes and frequencies of defects in PVC-U pipes, their causes, and the possibility of future rehabilitation as well as the improvement in the deterioration models.     

Evaluation of geocomposite compressible layers as induced trench method applied to shallow buried pipelines

This paper suggests as a rather simple and innovative alternative of the induced trench method with the use of geocomposite replacing EPS geofoam for protection of shallow buried pipes. Laboratory model tests and the numerical studies have been conducted on induced trenches constructed with relatively thin drainage geocomposite, as compressible layers, placed into sand. A parametric study using numerical modelling was conducted considering different arrangements of compressible layers in order to optimize the use of these geosynthetics in rehabilitation and maintenance of shallow buried pipes. It was concluded that geocomposites have compressibility enough to replace EPS using diminished area, which favor the applicability for shallow pipelines protection. Reduction on vertical soil pressures over the crown of the pipe reached values of 90%. The stress reduction at the crown was found to be significant affected by the width of the geocomposite and its distance from the crown of the pipe. The use of a more compressible condition of sand backfill provide more efficiency as far the geocomposite is from the crown of the pipe. Results from numerical modelling also indicate that using more than two geocomposite layers led to negligible stress reductions compared to one layer solution.