Tunnel Cracks Research Articles

A lightweight Mini-YOLOv5s algorithm for small target crack detection in tunnels

ABSTRACT The automatic detection and identification of tunnel cracks is very important for safe driving and tunnel maintenance. In order to accelerate the application of automated detection and reduce the deployment cost of target detection algorithm on edge devices, this paper adopts non-destructive testing and proposes a lightweight Mini-YOLOv5s algorithm for small target crack detection in tunnels, which mainly includes DP-C3 module, GDConv module, ECA-C3 module, CARAFE lightweight upsampling operator and loss function IGIoU. Among them, the DP-C3 module uses PConv to reduce the computational complexity of the model, and uses dilated convolution to enrich the receptive field to improve the information loss. The GDConv module improves the feature extraction capability of deep information. The feature fusion of small crack target information is realised by using ECA-C3 module and CARAFE lightweight up-sampling operator. A new loss function IGIoU is used to pay more attention to the contact degree of the boundary frame, so as to minimise the loss and improve the detection accuracy. Experiments show that the detection accuracy of Mini-YOLOv5s on self-made tunnel crack dataset reaches 91%, which compensates for the information loss, while the number of model parameters and GFLOPs are reduced by 35.4% and 30.4% respectively.

Study on crack law of shield segment under load variation based on XFEM

PurposeThe occurrence of segment cracks caused by load changes in shield tunnels would affect the safety of the tunnel structure. To this end, a three-dimensional fine shield tunnel segment model based on the extended finite element method (XFEM) is established.Design/methodology/approachThe cracking law of shield segment cracks is studied in two forms: overloading and unloading. The relationship between crack length, width and depth and transverse convergence and deformation is analyzed.FindingsThe results show that the cracks in shield tunnels mainly occur on the outer side of the arch waist and the inner side of the crown and bottom. Under overloading and unloading conditions, the length, width and depth of cracks increase non-linearly as the transverse convergence deformation increases. Under the same convergent deformation, the deeper the buried depth, the smaller the crack length, width and depth. Meanwhile, under overloading conditions, the influence of buried depth on the width and depth of cracks is more significant. In terms of crack width and depth, unloading conditions are more dangerous than overloading conditions.Originality/valueThe findings have a guiding effect for the management of cracks in shield tunnels during operation.

Evaluation of Leakage Due to Submerged Floating Tunnel Crack and Its Propagation Characteristics

Evaluation of Leakage Due to Submerged Floating Tunnel Crack and Its Propagation Characteristics

Evaluation method of tunnel cracking disease under biased pressure based on enhanced image fractal features

This paper focuses on the tunnel cracking disease under biased pressure. Through model tests on three lining conditions, the study investigates the expansion process of surface cracks on the lining under biased pressure. Proposing an improved method for calculating fractal characteristics of lining crack disease based on box counting dimension. Quantifying crack disease through digital image processing to obtain two parameters: total width and fractal dimension. Discussed the relationship between quantification parameters of surface crack disease in linings and the service performance of the lining. 29 groups of crack disease quantization parameters obtained by model test were selected to establish the disease sample space, and the spatial data were automatically learned and grouped based on clustering method. Finally, a new diagnostic Index of Tunnel Lining Defect Index-Crack & Dimension (TLDI-C&D) and its disease classification criteria for the whole lining ring are proposed. The results indicate that under the biased pressure, regardless of the presence of pre-existing defects, the biased pressure region is the most severely diseased area in terms of cracking. Compared to the intact lining, the initial cracking loads for arch crown defects and arch waist defects are advanced by 33.1 % and 73.0 %, respectively. After the apparent cracks in the lining are fully formed, pre-existing defects no longer affect the speed and distribution of crack propagation.The larger the fractal dimension, the more complex the morphology of the corresponding crack disease. Under the same image area, the ratio of crack quantity to width has a greater impact on the fractal dimension. There is a corresponding relationship between quantification parameters of crack disease and the bearing performance of the lining structure, making them indicators for measuring the load-bearing capacity of the lining. The most reasonable grading level for lining crack disease under the biased pressure is four levels. The diagnostic index for tunnel lining cracks, TLDI-C&D is 1.0～2.2 (level I), 2.2～3.2 (level II), 3.2～4.3 (level III), and 4.3～5.5 (level IV). On-site measurements have proven the accuracy of this crack disease diagnostic evaluation method.

Failure analysis and comparative study on tunnels under strike-slip fault and oblique-slip fault movements

To deeply study and compare the failure characteristics of tunnels under strike-slip and oblique-slip active faults, with the engineering background of a railway tunnel in western China crossing both strike-slip and oblique-slip active faults, two sets of large-scale indoor model tests with a geometric similarity ratio of 1:35 were conducted. The fissure development patterns and strain trends of the tunnel under the movement of both types of faults were monitored and analyzed. Meanwhile, a three-dimensional model of tunnel-rock-fault interaction was established using ABAQUS finite element analysis software to determine the variation patterns of bending moments and shear forces of the tunnel under the movement of both types of faults. The results showed that under the movement of strike-slip and oblique-slip active faults, circumferential bending fissures appear at the right waist of the hanging wall and the left waist of the footwall of the tunnel, and shear oblique fissures appear at the vault of the fault fracture zone. Among them, the number of cracks, the range of tunnel cracking and the degree of crack damage under the condition of the oblique-slip fault are obviously more serious than those under the condition of the strike-slip fault. Under the dislocation of the oblique-slip fault, a large area of broken zone appears in the vault of the fault fracture zone, the lining falls off, the steel bar is exposed, and there is an obvious uplift phenomenon. The right waist of the tunnel’s hanging wall mainly experiences tension while the left waist experiences compression, whereas on the hanging wall, the right waist experiences compression while the left waist experiences tension. Under the fault movement, the maximum bending moment value mainly occurs at a distance of 10 m from the fault fracture zone on both the hanging wall and the footwall, with the bending moment values at the fault fracture zone nearly approaching zero. The maximum shear force value occurs at the fault fracture zone. Under the movement of both types of faults, the failure mode of the tunnel is mainly characterized by bending failure of the hanging wall and the footwall and shear failure at the fault fracture zone. Compared to strike-slip active faults, the development of fissures in tunnels under oblique-slip active faults is more severe, with larger bending moment and shear force values, and a wider range of tunnel failure. These results can provide theoretical and technical experience for future similar projects.

Enhancing tunnel crack detection with linear seam using mixed stride convolution and attention mechanism

Cracks in tunnel lining structures constitute a common and serious problem that jeopardizes the safety of traffic and the durability of the tunnel. The similarity between lining seams and cracks in terms of strength and morphological characteristics renders the detection of cracks in tunnel lining structures challenging. To address this issue, a new deep learning-based method for crack detection in tunnel lining structures is proposed. First, an improved attention mechanism is introduced for the morphological features of lining seams, which not only aggregates global spatial information but also features along two dimensions, height and width, to mine more long-distance feature information. Furthermore, a mixed strip convolution module leveraging four different directions of strip convolution is proposed. This module captures remote contextual information from various angles to avoid interference from background pixels. To evaluate the proposed approach, the two modules are integrated into a U-shaped network, and experiments are conducted on Tunnel200, a tunnel lining crack dataset, as well as the publicly available crack datasets Crack500 and DeepCrack. The results show that the approach outperforms existing methods and achieves superior performance on these datasets.

Vehicle Self-Position Estimation Using Lighting Recognition in Expressway Tunnel for Visual Inspection Flow

In this study, a stable and high-speed vision-based self-position estimation method was proposed that improves upon the existing method of lane detection by recognizing the lighting facilities that are installed in tunnels on Japanese expressways where GNSS cannot be used. In addition, we proposed a method for inspecting multiple cracks at once by estimating the self-position with the successful rate 75% in the traveling direction by counting the lighting with the successful rate 99.85%. The effectiveness of the method was verified by capturing images of cracks in an actual tunnel. The proposed method will enable more frequent inspections for tunnel cracks that lead to flaking while maintaining infrastructure safety, reducing costs, and improving tunnel visual inspection flow efficiency.

Subway Tunnel Crack Identification based on YOLOv5

Subway Tunnel Crack Identification based on YOLOv5

Effective small crack detection based on tunnel crack characteristics and an anchor-free convolutional neural network

Tunnel cracks are thin and narrow linear targets, and their pixel proportions in images are usually very low, less than 6%; therefore, a method is needed to better detect small crack targets. In this study, a crack detection method based on crack characteristics and an anchor-free framework is investigated. First, the characteristics of cracks are analyzed to obtain the real crack texture, interference noise texture, and targets appearing near each crack as the context information for the model to filter and remove noise. We discuss the crack detection performance of anchor-based and anchor-free algorithms. Then, an optimized anchor-free algorithm is proposed in this paper for crack detection. Based on the advantages of YOLOX-x, we add a semantic enhancement module to better use contextual information. The experimental results show that the anchor-free algorithm performs slightly better than other algorithms in crack detection situations. In addition, the proposed method displays better detection performance for slender and inconspicuous cracks, with an average precision of 0.858.

Crack Width Recognition of Tunnel Tube Sheet Based on YOLOv8 Algorithm and 3D Imaging

Based on the tunnel crack width identification, there are operating time constraints, limited operating space, high equipment testing costs, and other issues. In this paper, a large subway tunnel is a research object, and the tunnel rail inspection car is an operating platform equipped with industrial cameras in order to meet the requirements of the tunnel tube sheet crack width recognition of more than 0.2 mm, with the measuring instrument to verify that the tunnel rail inspection car in the state of uniform motion camera imaging quality has the reliability through the addition of laser rangefinders, the accurate measurement of the object distance and the calculation of the imaging plane and the angle of the plane to be measured, to amend the three-dimensional cracks. The pixel resolution of the image is corrected, the images imaged by the industrial camera are preprocessed, the YOLOv8 algorithm is used for the intelligent extraction of crack morphology, and finally, the actual width is calculated from the spacing between two points of the crack. The crack detection width obtained by image processing using the YOLOv8 algorithm is basically the same as the value of crack width obtained by manual detection, and the error rate of crack width detection ranges from 0% to 11%, with the average error rate remaining below 4%. Compared with the crack detection error rate of the Support Vector Machine (SVM), the crack extraction model is reduced by 1%, so using the tunnel inspection vehicle as a platform equipped with an industrial camera, YOLOv8 is used to realize the recognition of the shape and width of the cracks on the surface of the tunnel tube sheet to meet the requirements of a higher degree of accuracy. The number of pixels and the detection error rate are inversely proportional to each other. The angle between the imaging plane and the plane under test is directly proportional to the detection error rate. The angle between the vertical axis where the lens midpoint is located and the line connecting the shooting target and the lens center point is αi and the angle θi between the measured plane and the imaging plane is reciprocal, i.e., αi + θi = 90°. Therefore, using the inspection vehicle as a mobile platform equipped with an industrial camera and based on the YOLOv8 algorithm, the crack recognition of the tunnel tube sheet has the feasibility and the prospect of wide application, which provides a reference method for the detection of cracks in the tunnel tube sheet.

Automatic Road Tunnel Crack Inspection Based on Crack Area Sensing and Multiscale Semantic Segmentation

Automatic Road Tunnel Crack Inspection Based on Crack Area Sensing and Multiscale Semantic Segmentation

An Accurate Tunnel Crack Identification Method Integrating Local Segmentation and Global Fusion Detection

An Accurate Tunnel Crack Identification Method Integrating Local Segmentation and Global Fusion Detection

A Dual-Tree–Complex Wavelet Transform-Based Infrared and Visible Image Fusion Technique and Its Application in Tunnel Crack Detection

Computer vision methods have been widely used in recent years for the detection of structural cracks. To address the issues of poor image quality and the inadequate performance of semantic segmentation networks under low-light conditions in tunnels, in this paper, infrared images are used, and a preprocessing method based on image fusion technology is developed. First, the DAISY descriptor and the perspective transform are applied for image alignment. Then, the source image is decomposed into high- and low-frequency components of different scales and directions using DT-CWT, and high- and low-frequency subband fusion rules are designed according to the characteristics of infrared and visible images. Finally, a fused image is reconstructed from the processed coefficients, and the fusion results are evaluated using the improved semantic segmentation network. The results show that using the proposed fusion method to preprocess images leads to a low false alarm rate and low missed detection rate in comparison to those using the source image directly or using the classical fusion algorithm.

Numerical Analysis of Low-Cost Recognition of Tunnel Cracks with Compressive Sensing along the Railway

Currently, the use of microseismic detection technology for crack detection and localization in rock masses has great potential in detecting structural damage. As engineering safety has always been a very important issue, this study investigated the problem of multi-crack identification in rock masses within the environment of track tunnels using transient waves. A tunnel rock was modeled using MIDAS GTS NX software (2019.v1.2) and a crack transient wave model in the frequency domain was obtained through data analysis and simulation. Then, this was combined with compressive sensing techniques to locate and detect multiple cracks in tunnel rock. The performance of the proposed approach was validated through experimental simulations, which included experiments on differences in the number of cracks, as well as spatial samples. The experimental results indicate that the technique performs well for single-crack localization in tunnel rock mass, where the average localization error is 4 m. Meanwhile, the localization error is larger in multi-crack localization, and the number of spatial sample points set using compressive sensing also has a large impact on the experimental results.

Experimental and numerical studies on low-velocity impact of laminated C/SiC structures

Laminated C/SiC(carbon fiber reinforced silicon carbide composites) has the complicated nature of composite failure which include cracks for void defects at the meso and micro scales, complex cracks propagation of tunnel cracks, matrix cracks, delaminations and fiber cracks, orthotropic, and pseudoplastic behavior. It has technical challenge to solve all of them for complicated-mechanics problems by using multi-scale methodology. This paper focused tackling it by a model that lies between macro and meso scales, and proposed a new conceptual 3D FE-PDM(Finite Element - Progressive Damage Method). 3D FE-PDM was validated by the experiments of low-velocity impact of 2D C/SiC and 3D needled C/SiC. Damage initiation and evolution were controlled by strain in order to preventing stress chaos which could occur as cracks leading local stress relief. The multi-linear constitutive relations were adopted in orthotropic directions. The coupled effects of stresses between in-plane and out-of-plane were computed by adding damage coefficients into stiffness matrix. Cohesive Zone Method was used for delamination. The PDM approach was introduced for the 3D elements for intralaminar and cohesive elements for the interfaces. A parametric inversion methodology was put forward to determine the model parameters iteratively by comparing simulation results with the load-time curves, damage CT details of the experiments. Validation implies that, with small number of parameters, 3D FE-PDM is able to predict damage initiation of each phase knee, damage evolution, and load–displacement curve with good convergence for the low-velocity impact case. 2D woven and 3D needled non-woven C/SiC both have impact energy threshold and different damage evolution types.

Tunnel Crack Detection Method and Crack Image Processing Algorithm Based on Improved Retinex and Deep Learning.

Tunnel cracks are the main factors that cause damage and collapse of tunnel structures. How to detect tunnel cracks efficiently and avoid safety accidents caused by tunnel cracks effectively is a research hotspot at present. In order to meet the need for efficient detection of tunnel cracks, the tunnel crack detection method based on improved Retinex and deep learning is proposed in this paper. The tunnel crack images collected by optical imaging equipment are used to improve the contrast information of tunnel crack images using the image enhancement algorithm, and this image enhancement algorithm has the function of multi-scale Retinex decomposition with improved central filtering. An improved VGG19 network model is constructed to achieve efficient segmentation of tunnel crack images through deep learning methods and then form the segmented binary image. The Zhang-Suen fast parallel-thinning method is used to obtain the skeleton map of the single-layer pixel, and the length and width information of the tunnel cracks are obtained. The feasibility and effectiveness of the proposed method are verified by experiments. Compared with other methods in the literature, the maximum deviation in the length of the tunnel crack is about 5 mm, and the maximum deviation in the width of the tunnel crack is about 0.8 mm. The experimental results show that the proposed method has a shorter detection time and higher detection accuracy. The research results of this paper can provide a strong basis for the health evaluation of tunnels.

Tunnel Lining Crack Detection Method Based on Polarization 3D Imaging

Non-contact and non-destructive polarization 3D imaging uses a passive, single-frame array image to calculate 3D information, making it possible to obtain high-precision 3D information about tunnel cracks, and offering outstanding technical advantages. Based on the introduction of the principle of crack detection with polarization 3D imaging, a tunnel lining crack detection plan was developed and a detection equipment was designed. The method and process of polarization 3D imaging for lining crack detection are described in detail. A model of the impact of the tunnel environment on 3D detection and a method for obtaining absolute information have been established to obtain high-precision 3D information about cracks. In a real tunnel environment, tests were conducted to detect wide cracks, narrow cracks, and artificial cracks. The crack detection accuracy with respect to the crack width was 0.2–0.3 mm, and with respect to crack length was 0.2–0.3 mm. At the same time, crack depth information could be obtained. The present research results can provide technical support for the application of polarization 3D imaging in tunnel crack detection.

Numerical Computing Research on Tunnel Structure Cracking Risk under the Influence of Multiple Factors in Urban Deep Aquifer Zones

During the operation period of tunnels in urban deep aquifer zones, the geological environment around the tunnel is complex and the surrounding strata are rich in groundwater, which often poses a risk of structure cracking and groundwater leakage, seriously threatening the tunnel’s safety. To reduce the risk of tunnel cracking, a theoretical calculation model and a three-dimensional concrete–soil interaction thermo-mechanical coupling numerical computing model was established to analyze the tunnel structure cracking risk under the influence of multiple factors in urban deep aquifer zones. The response mechanism of structural stress and deformation under the influence of the grade of rock and soil mass, overburden thickness, temperature difference, structure’s length–height ratio, structure’s thickness, and structure’s elastic modulus was investigated, and the stress and deformation response characteristics of the structure with deformation joints were explored. The results show that the maximum longitudinal tensile stress of the structure increases with the increase in the grade of rock and soil mass, overburden thickness, temperature difference, structure’s length–height ratio, and elastic modulus. The temperature difference has the most significant impact on the longitudinal tensile stress of the structure, with the maximum tensile stress of the structure increasing by 2.8 times. The tunnel deformation joints can effectively reduce the longitudinal tensile stress of the structure, and the reduction magnitude of the tensile stress is the largest at the deformation joints, which is 64.7%.

Statistical and numerical analysis on characteristics and influence factors of construction cracks of large-diameter underwater shield tunnel: a case study

Tunnel excavation is prone to segment cracks, which may lead to serious safety accidents. This work aims to investigate the type, location and occurrence time of cracks in a large-diameter underwater shield tunnel and also ascertain the causes of the cracks. The correlations between crack characteristics and shield tunneling parameters were analyzed. The effect of jack deflection and poor assembly on cracks were investigated using a refined finite element model of two consecutive rings with consideration of the assembly clearance and the constraint effect of the shield machine. The results confirmed that 93% of the cracks were formed between the current ring and the last ring jacking stage. Positive cracks induced by jack deflection occurred during the current ring jacking stage, while the lengths of the cracks, proportional to the constraint of the boring machine on the segments, remained constant during the subsequent construction process. The cracks caused by poor assembly quality arose during the current and next ring jacking stages. Furthermore, the cracks generated in the current ring jacking stage would propagate during the subsequent construction process. The type, length, and number of cracks were influenced by the assembly clearance. The inverted V-shape longitudinal joints caused more cracks than any other assembly defects. The finding of this study results and conclusions are expected to contribute to the reduction of cracks at the jacking stage of the large-diameter underwater shield tunnels with eccentricity force transfer platforms.

Automated pixel-level crack monitoring system for large-scale underground infrastructure – A case study at CERN

In concrete tunnel linings, cracks usually appear and develop as an early sign of structural degradation prior to severe intolerable serviceability damage. The monitoring and assessment of crack spatial distribution can highlight long-term tunnel structural behavior and facilitate tunnel maintenance. This study describes a remote and automated system for conducting crack monitoring at a pixel-level scale using robot-mounted imaging technology. This system collects crack images remotely and stitches them together to create a panorama image of the tunnel surface. Employing transfer learning, this study fine-tunes and improves the state-of-the-art semantic segmentation model with a lightweight backbone, DeepLab V3plus, to detect cracks automatically. A novel smooth blending prediction method is implemented on the panorama to present long-distance tunnel crack distribution, alleviating misclassification problems encountered in high-resolution image inference. In addition, transfer learning, tailored loss functions, and regularization techniques have been developed based on the CERN tunnel crack database characteristics to maintain high performance and generalization of the proposed method.Field trials conducted in tunnels at CERN demonstrate the feasibility of the proposed crack monitoring system. Results show that the proposed system allows the identification of severe crack-damaged tunnel sections and specific crack patterns, which can be related to the structural behavior of the tunnel lining.