Tunnel Inspection Research Articles

Retracted: Cable tunnel unmanned aerial vehicle location and navigation method based on ultra wideband and depth camera fusion

AbstractWith the increasing demand for electric energy, more and more cities are constructing cable tunnels. In order to prevent the cable in the tunnel from being damaged, regular inspection of the cable tunnel is very important. However, due to the large scale of cable tunnel construction, the traditional manual inspection is difficult to meet the maintenance requirements. With the development of intelligent technology, the intelligent patrol mode of UAV has gradually replaced the traditional manual patrol mode and become the main patrol mode of cable tunnel. Therefore, the positioning and navigation method of cable tunnel UAV is studied in this paper. By using UWB localization algorithm and depth camera fusion, the positioning and navigation methods of UAVs are determined and compared with the positioning algorithm based on optical flow. Experimental comparison results show that the maximum and minimum yaw angles of UAV positioning based on the proposed algorithm are 2.41° and 0.16°, while the maximum and minimum yaw angles of UAV positioning based on the comparison algorithm are 3.11° and 1.51°. It can be seen that the integration of UWB and depth camera is of great significance for the research of positioning and navigation methods of cable tunnel UAVs.

Deep Learning-Based Defect Detection Framework for Ultra High Resolution Images of Tunnels

This study proposes a defect detection framework to improve the performance of deep learning-based detection models for ultra-high resolution (UHR) images generated by tunnel inspection systems. Most of the scanning technologies used in tunnel inspection systems generate UHR images. Defects in real-world images, on the other hand, are noticeably smaller than the image. These characteristics make simple preprocessing applications, such as downscaling, difficult due to information loss. Additionally, when a deep learning model is trained by the UHR images under the limited computational resource for training, problems may occur, including a reduction in object detection rate, unstable training, etc. To address these problems, we propose a framework that includes preprocessing and postprocessing of UHR images related to image patches rather than focusing on deep learning models. Furthermore, it includes a method for supplementing problems according to the format of the data annotation in the preprocessing process. When the proposed framework was applied to the UHR images of a tunnel, the performance of the deep learning-based defect detection model was improved by approximately 77.19 percentage points (pp). Because the proposed framework is for general UHR images, it can effectively recognize damage to general structures other than tunnels. Thus, it is necessary to verify the applicability of the defect detection framework under various conditions in future works.

Motion control and positioning system of multi-sensor tunnel defect inspection robot: from methodology to application

As the mileage of subway is increasing rapidly, there is an urgent need for automatic subway tunnel inspection equipment to ensure the efficiency and frequency of daily tunnel inspection. The subway tunnel environment is complex, it cannot receive GPS and other satellite signals, a variety of positioning sensors cannot be used. Besides, there are random interference, wheel and rail idling and creep. All the above results in poor performance of conventional speed tracking and positioning methods. In this paper, a multi-sensor motion control system is proposed for the subway tunnel inspection robot. At the same time, a trapezoidal speed planning and a speed tracking algorithm based on MPC (Model Predictive Control) are proposed, which simplify longitudinal dynamics model to overcome the complex and variable nonlinear problems in the operation of the maintenance robot. The optimal function of speed, acceleration and jerk constraint is designed to make the tunnel inspection robot achieve efficient and stable speed control in the subway tunnel environment. In this paper, the "INS (inertial navigation system) + Odometer" positioning method is proposed. The difference between the displacement measured by the inertial navigation system and the displacement calculated by the odometer is taken as the measurement value, which reduces the dimension of the conventional algorithm. The closed-loop Kalman filter is used to establish the combined positioning model, and the system error can be corrected in real time with higher accuracy. The algorithms were verified on the test line. The displacement target was set to be 1 km and the limit speed was 60 km/h. The overshooting error of the speed tracking algorithm based on trapezoidal velocity planning and MPC was 0.89%, and the stability error was 0.32%. It improved the accuracy and stability of the speed following, and was much better than the PID speed tracking algorithm. At the speed of 40 km/h, the maximum positioning error of the robot within 2 km is 0.15%, and the average error is 0.08%. It is verified that the multi-sensor fusion positioning algorithm has significantly improved the accuracy compared with the single-odometer positioning algorithm, and can effectively make up for the position error caused by wheel-rail creep and sensor error.

Inspeção de estruturas de pontes e túneis baseada em VANTs: uma revisão das aplicações

abstract: Bridges and tunnels are large and complex structures that demand periodic inspections to assess their physical conditions. Although both have different designs and constructions from each other, a common problem they share is the drawbacks that their conventional inspections face. Moreover, conventional procedures not only are laborious, time-consuming, and costly, but also involve high and/or hard-to-reach places, often exposing the specialized inspectors to danger. To overcome these problems, the Unmanned Aerial Vehicle (UAV) is being explored to automate these inspections. Recently, the number of researches employing it within the civil infrastructure condition assessment has been growing in recent years, especially for the inspection of large and complex structures. Unlike the UAV-based bridge inspection that already has some review articles available in the literature, there are none yet for the tunnel inspection, to the best of authors' knowledge. Therefore, this article intends to conduct not only a review of the few UAV-based tunnel inspection researches available in the literature, but also an up-to-date review of UAV-based bridge inspection researches. Finally, the key challenges and future trends of the UAV-based inspection of these two structures are discussed, followed by the review conclusions.

Artificial Intelligence and the UK Construction Industry – Empirical Study

There is a lack of research on the use of Artificial Intelligence (AI) within the construction sector in the UK. Therefore, this research seeks to explore AI uses and its benefits on the UK construction industry. Given the new and unexplored nature of the research problem, a qualitative case study research methodology was adopted. Construction projects, which have adopted some of the AI forms all around the UK, are investigated extracting the use of the technology and how processes within the construction industry can benefit from the adoption of AI. Developing an AI system can benefit the construction industry in terms of the planning process, organizations have implemented AI systems to accomplish tasks such as tunnel inspections, safety hazard identifying, and risk management. These implementations have proved to be successful and efficient in improving production within the organizations. This paper highlights the key uses and benefits of AI-based systems within the construction industry. The business model was developed based on current work practices with AI and without AI. It is concluded that the industry as a whole should enhance coordination and cooperation across the value chain and agree on common goals and standards for the adoption of AI.

Key technology for the construction and inspection of long-distance underwater tunnel for 1000 kV gas-insulated transmission line

Key technology for the construction and inspection of long-distance underwater tunnel for 1000 kV gas-insulated transmission line

Automatic Identification and Intelligent Optimization of Tunnel-Free Curve Reconfiguration

Extracting complete cross-sectional geometric features from the large amount of point cloud data acquired by laser scanners plays an important part in the detection of deformations in tunnel inspection projects. Tunnel cross-sections have symmetrical geometric features, and information is traditionally collected manually. The traditional manual extraction of point clouds is inefficient and limited by the subjectivity of the operators when addressing the problems. This paper proposes a new algorithm for the automatic identification of tunnel lining section curves, the rapid separation of common interference targets, and the optimization of curve geometry features. The innovation of this approach lies in the combination of B-spline and Euclidean clustering methods and the comprehensive evaluation of the denoising results in terms of precision, recall, F-score, and rand index (RI). In this way, the automatically extracted health point cloud data are refitted to optimize the tunnel profile model.

Water leakage mapping in concrete railway tunnels using LiDAR generated point clouds

Detection of water leakage areas is one of the most important tasks of regular underground tunnel inspections. Current manual visual assessments are plagued by reduced efficiency and poor accuracy, requiring personnel to physically access tunnels to conduct assessments. This form of monitoring is no longer sustainable and thus a more cost-effective method is required. Light detection and ranging (LiDAR) is one such method and commonly known for its ability to generate high-density 3D coordinated point clouds of scanned environments. In addition to point coordinates an intensity value, highly dependent on surface properties and the backscattered energy of the laser beam, is recorded. In this study properties such as surface colour, roughness and state of saturation are reviewed. Results of systematic tests of different colour and concrete targets, scanned using a mobile LiDAR scanner, are discussed. The aim of the research was to prove the aptitude for remote sensing of water leakage areas in underground tunnels and provide an automated workflow to extract quantitative information of each leak. Field tests demonstrated the ability to rapidly capture, identify, extract and record areas of water leakage based on the intensity and spatial information of tunnel point cloud data. This is particularly useful as water ingress is known to degrade concrete, resulting in the earlier onset of corrosion, spalling and loss of strength. The LiDAR scanner used here proved capable of reducing survey time and provided inspectors with a complete 3D model of the tunnel which was supplemented with quantitative leakage information (location and area).

Angle of View Switching Method at High-Speed Using Motion Blur Compensation for Infrastructure Inspection

Efficient imaging is achieved under conditions of high relative velocity between the camera and the subject by using the following imaging system; two galvanometer mirrors are placed vertically in front of the camera, one for motion blur compensation and the other for switching the angle of view. The proposed system can overcome the shortcomings of conventional imaging systems with motion blur compensation, such as a small angle of view, and efficiently acquire high-resolution images. If the angle is changed for each capture while the mirrors are stationary for the exposure time, the natural frequency of the mirrors produces noise, leading to a poor resolution. However, this issue can be managed by generating and using an input that does not contain a natural frequency component. A target moving in one dimension can be captured and it is confirmed that the angle of view was extended from the obtained image. It is expected that the camera will be used for inspections under conditions where the relative speed between the camera and target is high, such as in highway tunnel inspections.

Discussion on How to Do Well the Test and Inspection of Highway Bridges and Tunnels

Discussion on How to Do Well the Test and Inspection of Highway Bridges and Tunnels

First insights into the human-related risks of tunnel inspection

Whereas human factors in the non-destructive testing (NDT) of metallic components are a poorly investigated topic (in comparison to other industrial fields such as aviation), human factors in the inspection of concrete components are even less known. Studies have shown that there is always some variability between individuals in their inspection results and that human factors affect the reliability of NDT inspections. And even though those human factors (the effects of and interaction between technology, organisation, environment, and individual characteristics) do not necessarily lead to negative inspection outcomes, their understanding is a vital step towards preventing possible structure-breaking failures and thereby ensuring the safety of industry, environment and infrastructure. To identify the possible human-related risks in the tunnel inspection, a systematic approach to risk management has been adopted. This involves identification of risks, their characterisation, and suggestions for risk treatment. In line with this approach, the inspections will be observed followed by interviews with inspectors to collect initial information about the inspections in the field and possible performance shaping factors. Furthermore, a human-oriented Failure Modes and Effects Analysis (FMEA) will be used to identify possible human-related risks throughout data collection and evaluation and to evaluate them with respect to their possible causes, consequences, the probability of their occurrence and with respect to existing and possible preventive measures. The results aim to increase our overall understanding of human factors related to NDT, provide first insights and understanding of human factors in tunnel inspection and suggest measures to prevent human error in that application.

Automatic Tunnel Crack Inspection Using an Efficient Mobile Imaging Module and a Lightweight CNN

Cracks in tunnel linings are the most common tunnel defects. As early indicators of structural deterioration, cracks represent critical problems for the safety of tunnels. Several mobile tunnel inspection systems (MTISs) have been developed for tunnel crack inspection. However, due to the weak signals of cracks, these MTISs require considerable exposure time to capture high-quality tunnel images, necessitating a low travel speed. Meanwhile, traditional crack detection methods encounter difficulties in processing tunnel crack images because of their low contrast and poor continuity. To overcome these challenges, this study presents a new MTIS for fast tunnel crack inspection that consists of a novel mobile imaging module and an automatic crack detection module. The imaging module is composed of an array of high-resolution charge-coupled device (CCD) cameras, a mobile laser scanner, and a lighting array. The core of the crack detection module is a novel lightweight convolutional neural network (CNN) designed for efficient tunnel crack detection, with an effective spatial constraint strategy to guarantee crack continuity. We collected a new tunnel crack dataset consisting of 1,218 images using our mobile imaging module at a driving speed of 80 km/h. Comprehensive experiments were conducted on this dataset to evaluate the performance of our proposed network. The results demonstrate that the presented CNN can effectively detect tunnel cracks with state-of-the-art performance, achieving an F1-score greater than 0.88 and an inference speed of 17 FPS with only 3.4M model parameters. The code and data are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/urban-informatics/LinkCrack</uri> .

Remote inspection method for water conveyance tunnel based on single-beam scanning sonar

Water conveyance tunnels perform an important role in water-diversion projects. With a water conveyance tunnel transferring water, the conflict between urban crowded ecology and agricultural water can be relieved. Research shows that there is a significant need for a more objective and accurate inspection of water conveyance tunnels during operation. Hence, we investigate the use of a single-beam scanning sonar to detect a water conveyance tunnel and generate a three-dimensional point cloud model, which can be used to make objective predictions for surface defects. First, a cobweb-like grid matrix was proposed to grid discrete points to establish the topological relationship of the point cloud. Second, a denoising method fusing acoustic properties and intensity was used to filter the noise in the acoustic point cloud. Third, an optimal echo extraction technology based on a second-order differential model was proposed to obtain a high-precision point cloud of the tunnel. Experiments show that the proposed denoising method and optimal echo extraction method are superior to traditional algorithms commonly used in ground point clouds, with good results achieved at real test sites.

Multispectral water leakage detection based on a one-stage anchor-free modality fusion network for metro tunnels

Water leakage detection has remained one of the high-priority research topics for metro tunnel inspection. Previous research mainly uses a single visible light camera as a sensor to detect the leakage regions in the image, which is extremely sensitive to inadequate illumination inside the tunnel. To robustly and accurately detect these water leakage defects in metro tunnels, we propose a novel detection method based on multispectral modality fusion that combines the advantages of visual-optical (VIS) and thermal infrared (IR) sensors. First, a multispectral data collection system is designed, and a four-dimensional water leakage dataset containing 1840 pictures is collected and labeled. Second, a simple and efficient one-stage anchor-free multispectral modality fusion network is proposed for water leakage detection. The proposed method consists of single-modality feature extraction and multimodality feature fusion based on a feature pyramid network (FPN). Finally, the VIS/IR single modality verification experiment proves that the VIS/IR based detector has unavoidable disadvantages in water leakage detection. The multispectral detection experiment proves that our proposed modality fusion detector achieves an approximately 3.35% lower average miss rate than the state-of-the-art method and can accurately and robustly detect leakage defects not affected by light conditions.

A crack detection system of subway tunnel based on image processing

For the images of crack defects of subway tunnel, traditional image processing algorithms is hardly effective for dealing with problems existing in the image like uneven illumination or severe noise interference. Based on pixel-level processing, an improved crack detection algorithm is proposed using structural analysis for improving the quality of tunnel images. Firstly, image preprocessing transforms the raw images of tunnel surface into binary images containing crack pixels and noise pixels. To extract crack information from binary images, three kinds of interference components are removed by structural analysis. With few interference components remaining in the image, the width of crack can be calculated according to the mean and standard deviation of the local area of the crack. Based on the algorithm, a crack detection system is designed, and a tunnel inspection experiment is conducted in a subway tunnel to capture tunnel surface images. Compared with popular image processing method, the crack recognition rate of the proposed method is 91.15% which is approximately 10% higher than others, and the measurement result of crack width based on the proposed method is closer to the ground truth. The experiment result indicates that the proposed method shows a better performance in crack detection.

Design and Experimental Evaluation of an Aerial Solution for Visual Inspection of Tunnel-like Infrastructures

Current railway tunnel inspections rely on expert operators performing a visual examination of the entire infrastructure and manually annotating encountered defects. Automatizing the inspection and maintenance task of such critical and aging infrastructures has the potential to decrease the associated costs and risks. Contributing to this aim, the present work describes an aerial robotic solution designed to perform autonomous inspections of tunnel-like infrastructures. The proposed robotic system is equipped with visual and thermal sensors and uses an inspection-driven path planning algorithm to generate a path that maximizes the quality of the gathered data in terms of photogrammetry goals while optimizing the surface coverage and the total trajectory length. The performance of the planning algorithm is demonstrated in simulation against state-of-the-art methods and a wall-following inspection trajectory. Results of a real inspection test conducted in a railway tunnel are also presented, validating the whole system operation.

Speed Improvements of Tunnel Inspection System with Swing and Telescopic Arm

Speed Improvements of Tunnel Inspection System with Swing and Telescopic Arm

3D Laser Triangulation and Deep Learning Approach to Tunnel Inspection

Regular inspection of tunnel surfaces is an important practice from both a safety and tunnel asset management perspective. However, inspection for cracking and spalling is still predominantly a manual task, which is time consuming, subjective, and exposes on-foot staff to risk. This presentation will explore the use of 3D laser scanning technology and artificial intelligence to automate the inspection process with a Canadian metro case study being presented.

Research on the Present Situation and Development Trend of Subway Tunnel Inspection Vehicle

For the operational subway tunnel, the manual inspection accounts for the majority in terms of detecting the diseases and damage of tunnel. The accuracy of manual inspection mainly depends on the professional level of the detection personnel, and the whole detection process always is inefficient, which cannot meet the needs of actual tunnels. To address this issue, the intelligent mobile tunnel detection vehicle emerges as the times require. By using advanced technologies such as laser scanning and high-speed camera array, the subway tunnel detection vehicle has achieved the advantages of simple operation, comprehensive function and automatic detection. However, the current subway tunnel detection vehicle mainly realizes the scanning detection of tunnel surface diseases, and the detection of tunnel structural diseases is less involved. Based on the track and tunnel detection requirements, this study analyzes the current situation and existing problems of subway tunnel detection comprehensively, puts forward the development direction of tunnel structure detection, and the application prospect of intelligent detection vehicle in subway tunnel is prospected.

Evaluating Deterioration of Tunnels Using Computational Machine Learning Algorithms

AbstractTunnels are an integrated part of the transportation infrastructure. Structural evaluation and inspection of tunnels are vital tasks to assess the deterioration of tunnels and maintain thei...