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.     

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.     

Neuro-fuzzy network for the classification of buried pipe defects

Pipeline infrastructure is decaying at an accelerating rate due to reduced funding and insufficient quality control resulting in poor installation, little or no inspection and maintenance, and a general lack of uniformity and improvement in design, construction and operation practices. The current practice that is being followed to inspect the conditions of pipes is usually time consuming, tedious and expensive. It may also lead to diagnostic errors due to lack of concentration of human operators. Buried pipe defect classification is thus a practical and important pattern classification problem. These defects appear in the form of randomly shaped cracks and holes, broken joints and laterals, and others. This paper proposes a new neuro-fuzzy classifier that combines neural networks and concepts of fuzzy logic for the classification of defects by extracting features in segmented buried pipe images. A comparative evaluation of the K-NN, fuzzy K-NN, conventional backpropagation network, and proposed neuro-fuzzy projection network classifiers is carried out. Among the five neural methods implemented and tested, the proposed neuro-fuzzy classifier performs the best, with classification accuracies around 90% on real concrete pipe images.     

Iterative application of generative adversarial networks for improved buried pipe detection from images obtained by ground-penetrating radar

Ground-penetrating radar (GPR) is widely used to determine the location of buried pipes without excavation, and machine learning has been researched to automatically identify the location of buried pipes from the reflected wave images obtained by GPR. In object detection using machine learning, the accuracy of detection is affected by the quantity and quality of training data, so it is important to expand the training data to improve accuracy. This is especially true in the case of buried pipes that are located underground and whose existence cannot be easily confirmed. Therefore, this study developed a method for increasing training data using you only look once v5 (YOLOv5) and StyleGAN2-ADA to automate the annotation process. Of particular importance is developing a framework for generating images by generative adversarial networks with an emphasis on images that are challenging to detect buried pipes in YOLOv5 and add them to a training dataset to repeat training recursively, which has greatly improved the detection accuracy. Specifically, F-values of 0.915, 0.916, and 0.924 were achieved by automatically generating training images step by step from only 500, 1000, and 2000 training images, respectively. These values exceed the F-value of 0.900, which is obtained from training by manually annotating 15,000 images, a much larger number. In addition, we applied the method to a road in Shizuoka Prefecture, Japan, and confirmed that the method can detect the location of buried pipes with high accuracy on a real road. This method can contribute to labor-saving training data expansion, which is time-consuming and costly in practice, and as a result, the method contributes to improving detection accuracy.     

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。     

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.     

交通荷载作用下埋地承插口排水管道动力响应分析

近年来,由于市政排水管道灾变导致的道路坍塌事故频发,水泥混凝土管是目前应用最为广泛的市政排水管道,其在交通荷载作用下的力学响应特征尚不明确。基于ABAQUS有限元软件,建立了带承插口结构排水管道三维数值模型。在考虑承插口、橡胶圈和无限元吸收边界等的基础上计算分析了不同脉冲幅值、不同荷载作用位置和不同管道埋深对管道动力响应的影响。结果表明：管节处受力高度不连续,交通荷载对其作用位置两侧一节管长范围内的管道影响显著;承口和插口环向以受拉和受压为主;交通荷载作用位置对管顶、管底和管侧纵向Mises应力最大值无明显影响,但对管顶和管底纵向Mises应力分布有影响;管道纵向Mises应力及环向竖向应力与管道埋深成正比,应力增量与埋深增量成反比。计算结果为进一步研究交通荷载作用下排水管道的力学机理提供参考。     

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.     

Point vibration measurements for the detection of shallow-buried objects

A major UK initiative, entitled ‘Mapping the Underworld’, is seeking to address the serious social, environmental and economic consequences arising from an inability to locate accurately and comprehensively the buried utility service infrastructure without resorting to extensive excavations. Mapping the Underworld aims to develop and prove the efficacy of a multi-sensor device for accurate remote buried utility service detection, location and, where possible, identification. One of the technologies to be incorporated in the device is low-frequency vibro-acoustics, and application of this technique for detecting buried infrastructure is currently being investigated. Here, the potential for making a number of simple point vibration measurements in order to detect shallow-buried objects, in particular plastic pipes, is explored. Point measurements can be made relatively quickly without the need for arrays of surface sensors, which can be expensive, time-consuming to deploy, and sometimes impractical in congested areas.At low frequencies, the ground behaves as a simple single-degree-of-freedom (mass–spring) system with a well-defined resonance, the frequency of which will depend on the density and elastic properties of the soil locally. This resonance will be altered by the presence of a buried object whose properties differ from the surrounding soil. It is this behavior which can be exploited in order to detect the presence of a buried object, provided it is buried at a sufficiently shallow depth. The theoretical background is described and preliminary measurements are made both on a dedicated buried pipe rig and on the ground over a domestic waste pipe. Preliminary findings suggest that, for shallow-buried pipes, a measurement of this kind could be a quick and useful adjunct to more conventional methods of buried pipe detection.     

A numerical investigation into the effect of pressure on holes and cracks in water supply pipes

The effect of water pressure in a pipe on the rate of leakage from leak openings in the pipe is one of the main factors influencing leakage that is still not understood sufficiently. In this study, the behaviours of different types of leak openings (round holes and longitudinal and circumferential cracks) on pressurized pipes were investigated for different pipe materials (uPVC, steel, cast iron and asbestos cement) using finite element analysis. Linear elastic behaviour was assumed. The study found that (1) pipe stresses are significantly affected by a leak opening, and can easily exceed the material's yield strength in the vicinity of the opening; (2) round holes show the smallest expansion with pressure, followed by circumferential cracks and then longitudinal cracks; (3) the areas of all leak openings increase linearly with pressure; (4) longitudinal pipe stresses affect the behaviours of round holes and circumferential cracks, but not that of longitudinal cracks; and (5) the effect of pressure on a leak opening increases exponentially with increasing hole diameter or crack length. An equation is proposed for modelling the effect of pressure on individual leaks.     

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.     

The influence of geosynthetic reinforcement on the mechanical behaviour of soil-pipe systems

Buried pipes may transport substances that can be harmful to people and the environment. These structures may be subjected to damages caused by soil movements and external interference, such as surcharges and excavations. Different applications of geosynthetics have demonstrated that they can be used to protect buried pipes and to minimize the consequences of pipe burst. This paper discusses results of large scale laboratory tests on a flexible pipe buried in unreinforced and geosynthetic reinforced soils subjected to surface surcharges. The pipes were buried in a cohesionless soil and different types of reinforcements were tested, with a wide range of tensile stiffness values. The results obtained show that the arrangement of the reinforcement enveloping the pipe reduced significantly pipe displacements and deflections. The efficiency of the reinforcement depended on its type and physical and mechanical properties. The open geogrid tested showed less reinforcement efficiency due to the passage of soil particles through its aperture during the tests. A theoretical solution available for pipes in unreinforced soils was extended to the reinforced situation with good agreement between predictions and measurements and showed that the presence of the reinforcement is equivalent to the pipe being buried in a significantly stiffer unreinforced soil.     

Quantitative non-destructive evaluation of push-fit joints

Ultrasound, closed-circuit television (CCTV) and Panoramo^® are capable of inspecting drinking water pipes and joints of any pipe material. The three tools were tested for their accuracy and reproducibility for gap width sizing in double-socket push-fit joints. The tests were performed at laboratory scale (PVC pipes and joints) in the field (asbestos cement pipes and joints) and in three full-scale tests (PVC) inside pipes used to supply drinking water. In the laboratory tests both accuracy and reproducibility were evaluated. In the field and full-scale tests only reproducibility of the tools was tested. CCTV proved to be the most accurate and reproducible for the application. This straightforward approach is considered to be a surrogate measure for joint's condition.     

Quantitative road crack evaluation by a U-Net architecture using smartphone images and Lidar data

Road cracks are a major concern for administrators. Visual inspection is labor-intensive. The accuracy of previous algorithms for detecting cracks in images requires improvement. Further, the length and thickness of cracks must be estimated. Light detection and ranging (Lidar), a standard smartphone feature is used to develop a method for the completely automatic, accurate, and quantitative evaluation of road cracks. The two contributions of this study are as follows. To achieve the highest segmentation accuracy, U-Net is combined with data augmentation and morphology transform. To calculate the crack length and thickness, crack images are registered into Lidar color data. The proposed algorithm was validated using a public database of road cracks and those measured by the authors. The algorithm was 95% accurate in determining crack length. The coefficient of determination for thickness estimation accuracy was 0.98 addressing various crack shapes and asphalt pavement patterns.     

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。     

Structural performance of buried prestressed concrete cylinder pipes with harnessed joints interaction using numerical modeling

Broken prestressing wire wraps are the main cause of failure in buried prestressed concrete cylinder pipes (PCCP), which form the backbone of water and wastewater infrastructure networks in North America. Advanced numerical modeling using non-linear finite elements is used to model the effect of the number and location of broken wire wraps on the structural performance of Class 125-14, 96-in. PCCP. The modeling technique used is unique in that it considers full interaction between adjacent pipes with harnessed joints, as well as combined internal and external loading with full soil–pipe interaction. Performance indicators in the various components of PCCP are monitored as internal pressure is increased. A sensitivity analysis is presented for how manipulating the severity of the damage affects the failure pressure of the pipe. The results show that the internal fluid pressure required to cause failure can be as much as 34% lower when the damage is at the barrel of the pipe, and that the internal pressure that causes yielding of the wire wraps decreases by 66% as the damage worsens from 5 to 100 wire breaks.     

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.     

Encoder–decoder network for pixel‐level road crack detection in black‐box images

Timely monitoring of pavement cracks is essential for successful maintenance of road infrastructure. Accurate information concerning crack location and severity enables proactive management of the infrastructure. Black‐box cameras, which are becoming increasingly widespread at an affordable price, can be used as efficient road‐image collectors over a wide area. However, the cracks in these images are difficult to detect, because the images containing them often include objects other than roads. Thus, we propose a pixel‐level detection method for identifying road cracks in black‐box images using a deep convolutional encoder–decoder network. The encoder consists of convolutional layers of the residual network for extracting crack features, and the decoder consists of deconvolutional layers for localizing the cracks in an input image. The proposed network was trained on 427 out of 527 images extracted from black‐box videos and tested on the remaining 100 images. Compared with VGG‐16, ResNet‐50, ResNet‐101, ResNet‐200 with transfer learning, and ResNet‐152 without transfer learning, ResNet‐152 with transfer learning exhibited the best performance, achieving recall, precision, and intersection of union of 71.98%, 77.68%, and 59.65%, respectively. The experimental results prove that the proposed method is optimal for detecting cracks in black‐box images at the pixel level.     

静/动载作用下埋地管道力学性能的试验分析

针对埋地管道开展了静载和循环荷载试验,综合分析了荷载类型、加载角度、管道外径和管道材质等因素对管道力学与变形性能以及管周土压力分布规律的影响。结果表明:静载作用下,管周土中垂直土压力大小与加载位置关系密切,水平土压力受“土拱效应”影响显著;管道呈现水平向外鼓胀、垂直径向压缩的椭圆状变形,且承压板荷载越大,管道变形越严重,同时管顶“土拱效应”越显著。循环荷载对埋地管道上方土层的沉降影响明显大于静载;改变承压板角度时效果差异明显,当加载范围关于管道轴线对称时埋地管道所受影响显著。对比不同外径和材质的埋地管道,发现当厚径比相同时,管径越大,壁厚越大,弹性模量也越大,管道的抗变形性能也就越好;公称压力相同时,聚丙烯管道抗变形能力强于外径相等的高密度聚乙烯管道。