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.     

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.     

Comparison of Structural Deterioration Models for Stormwater Drainage Pipes

Abstract:   Structural deterioration of pipes is the continuing reduction of load bearing capacity, which can be characterized through structural defects. Structural deterioration has been a major concern for asset managers in maintaining the required performance of stormwater drainage systems in Australia. Condition assessment using closed circuit television (CCTV) inspection is often carried out to assess the deteriorating condition of individual pipes. In this study, two models were developed using ordered probit and neural networks (NNs) techniques for predicting the structural condition of individual pipes. The predictive performances were compared using CCTV data collected for a local government authority in Melbourne, Australia. The significant input factors to the outputs of both models were also identified. The results showed that the NN model was more suitable for modeling structural deterioration than the ordered probit model. The hydraulic condition, pipe size, and pipe location were found to be significant factors for this case study.     

Segmentation of Pipe Images for Crack Detection in Buried Sewers

Abstract:   Assessing the condition of underground pipelines such as water lines, sewer pipes, and telecommunication conduits in an automated and reliable manner is vital to the safety and maintenance of buried public infrastructure. To fully automate condition assessment, it is necessary to develop robust data analysis and interpretation systems for defects in buried pipes. This article presents the development of an automated data analysis system for detecting defects in sanitary sewer pipelines. We propose a three-step method to identify and extract cracks from contrast enhanced pipe images. This method is based on mathematical morphology and curvature evaluation that detects crack-like patterns in a noisy pipe camera scanned image. As cracks are the most common defects in pipes and are indicative of the residual structural strength of the pipe, they are the focus of this study. This article discusses its implementation on 225 pipe images taken from different cities in North America and shows that the system performs very well under a variety of pipe conditions.     

Cox proportional hazards modelling of blockage risk in vitrified clay wastewater pipes

Older wastewater systems often contain vitrified clay pipes and these pipes are responsible for a significant proportion of blockages. From previous research and site experience, it is known that age, installation period, pipe length, diameter, depth and joint type affect the risk of blockage. Using statistical analysis we extend this list of variables to include soil type, road/rail proximity and road type. The analysis is based on blockage data for 43,976 vitrified clay pipes over a 7 year period. A Cox proportional hazards modelling (PHM) approach using established and new explanatory variables finds that pipe depth, pipe length, pipe gradient, pipe diameter, submergence in water, joint type, pipe installation decade, soil type, road proximity to the pipe, pipe purpose, land use code and road type are associated with risk of blockage. Pipes with the highest risk of failure due to blockage are identified and their locations and risk level are visually represented on a colour coded map. This analysis approach can be used by asset managers working in cost-constrained environments to appropriately target inspection, plan maintenance and replacement programs.     

A decision support system for horizontal directional drilling

Horizontal directional drilling (HDD) was pioneered in the US in the early 1970s. Through the integration of vertical oil well drilling technology with modern survey and steering techniques, horizontal directional drilling methods have the capability of undertaking a wide range of pipe installations, from large-diameter river crossings to small-diameter cable pipes. HDD has gained in popularity, as it is able to install underground pipes with minimum impact on society and the environment. Although HDD has been used for over three decades, it is necessary to evaluate the suitability and appropriateness of HDD methods on a project-to-project basis by considering a large number of factors that influence this decision-making. The lack of a systematic tool contributes to the difficulty of making effective decisions. This paper discusses the development of the framework for a decision support system (DSS) to assist in the selection of appropriate equipment/technique for HDD projects. Decision making criteria and rules are developed through literature review, analysis of field data compiled from the completed HDD projects, and investigations of ongoing projects. This DSS is most appropriate for use by contractors and asset managers at project planning stages.     

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.     

软土地区采用大开挖埋设排水管道的技术措施

在软土地区大开挖埋设排水管道 ,管道埋设后往往会出现接口开裂 ,不仅造成施工阶段闭水试验检测失败 ,而且竣工运行中因管周土体随地下水渗入管内而泄空 ,导致地面坍塌和造成对地下水的污染。该文提出管道接口与管基采用刚、柔性连接和与刚性相匹配的地基处理方案进行防治 ,以策安全运行。     

A data mining approach to modelling of water supply assets

The economic and social costs associated with pipe bursts and associated leakage problems in modern water supply systems are rapidly rising to unacceptably high levels.Pipe burst risks depend on a number of factors which are extremely difficult to characterise. A part of the problem is that water supply assets are mainly situated underground, and therefore not visible and under influence of various highly unpredictable forces. This paper proposes the use of advanced data mining methods in order to determine the risks of pipe bursts. For example, analysis of the database of already occurred bursts events can be used to establish a risk model as a function of associated characteristics of bursting pipe (its age, diameter, material of which it is built, etc.), soil type in which a pipe is laid, climatological factors (such as temperature), traffic loading, etc.In addition to the immediate aid with the the choice of pipes to be replaced, the outlined approach opens completely new avenues in asset management: the one of asset modeling. The condition of an asset such as a water supply network deteriorates with age. With reliable risk models, addressing the evolution of risk with aging asset, it is now possible to plan optimal rehabilitation strategies in advance, before the burst actually occurs.     

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.     

An Ultrasonic Profiling Method for the Inspection of Tubular Structures

Abstract:   This article presents an approach for the internal inspection of tubular structures immersed in water through the use of sonar techniques, generating enhanced 3D graphs which represent the inner surface of these structures. These graphs not only show the inner contour of the pipe but also integrate the intensity of the echoes employed to create the profile. The enhanced profile is generated by superimposing the peak intensity from the returning echoes at the calculated x, y, and z coordinates where it reflected from the pipe wall. The proposed method is capable of showing anomalous conditions, inside pipes filled with liquid, with dimensions smaller than the theoretical lateral and axial resolution of the transducer, in contrast to traditional methods where these kinds of defects are not disclosed. The proposed inspection method and its capabilities were validated through the realization of simulations and experiments. The presented approach was particularly developed with the aim of scanning internal sections of pipes filled with liquid using rotary ultrasonic sonars, but it is expected that this research could be expanded to the inspection of other submerged structures, such as water tanks, or pressurized vessels.     

Sewer pipe defects diagnosis assessment using multivariate analysis on CCTV video imagery

Closed circuit television (CCTV) technology has been commonly used to inspect underground pipe defects, and high CCTV image quality is a prerequisite for accurate defect diagnosis. An acceptance criterion for CCTV inspection videos is critical for ensuring accurate diagnosis and preventing disputes between employers and contractors. This paper used multivariate statistical methods to evaluate the overall quality of CCTV images and to define an acceptance criterion for CCTV videos. Numerous CCTV images from a sewer inspection project were assessed and their quality, consisting of similarity in luminance and contrast distortions, was calculated by comparing a set of ideal images. Principal component analysis (PCA) and redundancy analysis (RDA) grouped the CCTV videos into homogeneous segments with similar image quality and provided a visual acceptance criterion for CCTV inspection videos. Furthermore, RDA triplot indicated that the contrast improvement of CCTV images can effectively enhance image quality and increase the diagnosis efficiency.     

Prediction models for serviceability deterioration of stormwater pipes

Reducing the number of annual blockages and the consequential flooding events is one of the most important tasks for stormwater pipe infrastructure managers in Australia. Blockages are more likely to occur with pipes experiencing serviceability deterioration, resulting in a reduction of hydraulic capacity. When changing from a problem-based approach to a proactive maintenance and rehabilitation (M&R) approach, the asset managers need predictive information on the serviceability condition of pipes in order to firstly prepare the necessary resources from limited annual budgets and, secondly, to allocate these resources for the maintenance of the deteriorated pipes as precisely as possible. This paper investigates the application of a Markov model and an ordinal regression model for predictions of serviceability deterioration of stormwater pipes. The first model provides the prediction at a network level, which satisfies the first requirement, and the second model predicts serviceability condition for individual pipes, given the attributes of the pipes, in order to satisfy the second requirement. Both models are calibrated using Bayesian inference and Markov Chain Monte Carlo (MCMC) simulation techniques on a dataset supplied from the City of Greater Dandenong, Australia.     

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.     

超浅层顶管施工引起路基地层移动数值模拟

覆盖土层薄的超浅层顶管穿越路基施工引起管道周围土体移动会对路面结构造成破坏。结合实际工程,运用有限元模拟超浅层顶管穿越路基引起的地层移动和现场地表变形监测,研究了管道摩擦力、注浆率、顶推力、路面交通荷载等因素对覆盖土层变形的影响。研究表明：地层移动是先隆起后沉降,覆盖土层下部的移动速度比表层的大;地表变形的有限元计算结果和现场实测数据基本吻合;超浅层顶管施工对浅埋覆盖路基土层移动,横断面地表沉降变形在工具管纵向通过2倍顶管外径后基本趋于稳定,横向地表沉降沿侧向分布近似为正态分布,主要影响范围在顶管轴线左右两侧各1.5倍顶管外径的范围内;变形要求严格的地面下进行超浅层顶管施工,可以通过有限元分析对周围环境影响程度的评价。     

探地雷达探测地下管道图像的定性研究

通过对生产实践中积累的不同管道材料、不同形状及不同充填条件下地下管道探地雷达图像的分析研究,总结了上述情形地下管道的探地雷达图像特征,总结对探地雷达地下管线探测资料进行定性解释的基本规律,以期对地下管道的性质及运行状况给出参考意见。     

A morphological approach to pipe image interpretation based on segmentation by support vector machine

This paper presents a new approach to automatic pipe inspection using pixel-based segmentation of colour images by support vector machine (SVM) coupled with morphological analysis of the principal connected component of the segmented image. The pixel-based segmentation method has been tested using RGB, HSB, Gabor and local window feature sets and is seen to work best with the HSB feature set. The morphological analysis allows the principal connected component of the segmented image to be decomposed into the pipe flow line region, the pipe joints and adjoining defects. Generalisations of the morphological operations of erosion and dilation are defined and some simple properties of them are derived. A fuzzy approach to pipe connection detection is also described.     

Nondestructive evaluation of the depth of surface-breaking cracks in concrete pipes

Surface-breaking cracks affect the material and structural properties of concrete pipes. Therefore, the nondestructive evaluation of the crack depth is important to assess the serviceability of these pipes, which are commonly used in underground infrastructure and trenchless installations (micro-tunneling). This paper presents theoretical, numerical and experimental results for the depth evaluation of surface-breaking cracks. The wall of a concrete pipe is represented as a plate in the numerical and the analytical studies. In the experiments, an ultrasonic piezoelectric transmitter is used as a source. The propagation of the ultrasonic pulse is analyzed using the wavelet transform. A newly proposed wavelet transmission coefficient (WTC) is measured using an equal spacing configuration for the crack depth evaluation in concrete pipes and concrete plates. The results from laboratory and in situ tests show good potential for the practical application of the WTC for the depth evaluation of surface-breaking cracks.     

Study on temperature field around heat pipe underground based on line heat source theory

Low and moderate geothermal resource can be found in most areas of the world and heat pump system as an efficient device to utilize the geothermal is prevalent. It is important to know temperature field around the heat-exchange pipelines for efficient conversion and use of geothermal energy. The temperature distribution around the heat pipe underground was studied by the heat source method in the paper. Based on the method, every heat pipe could be supposed as a heat source that would produce a corresponding temperature field, and the total temperature underground could be the superposition of every temperature- field. The temperature fields around heat pipes were given in the paper in different containing water rate of the soil, gap rate of the soil and different disposal of heat pipes. It would be beneficial for design and better operation of the heat pipe system underground to use geothermal resource.