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Automatic crack detection is always a challenging task due to the influence of stains, shadows, complex texture, uneven illumination, blurring, and multiple scenes [2]. In the past decades, scholars have proposed a variety of image-based algorithms to automatically detect cracks on concrete surfaces and pavement. In the early studies, most of the methods are based on the combination or improvement of traditional digital image processing techniques (IPTs) [3], such as thresholding [4,5,6] and edge detection [7,8,9,10]. However, these methods are generally based on the significant assumption that the intensities of crack pixels are darker than the background and usually continuous, which makes these methods difficult to use effectively in the environment of complex background noise [11,12]. In order to improve the accuracy and integrity of crack detection, the methods based on wavelet transform [13,14] are proposed to lift the crack regions. However, due to the anisotropic characteristics of wavelets, they may not deal well with cracks with large curvatures or poor continuities [2].
In recent studies, several minimal path methods [15,16] have also been used for crack detection. Although these methods make use of crack features in a global view [3] and achieve good performance, their main limitation is that seed points for path tracking need to be set in advance [17], and the calculation cost is too high for practical application.
Unet [32], as a typical representative of semantic segmentation algorithm, has achieved great success in medical image segmentation. There are many similarities between pavement crack detection and medical image segmentation, so it is natural to apply Unet to pavement crack segmentation.
The patchwise detection method, which divides the original pavement images into many small patches, is adopted by more researchers due to its two advantages. First, more data can be generated, and second, the localization information of cracks can be obtained. Zhang et al. [39] proposed a six-layer CNN network with four convolutional layers and two fully connected layers and used their convolutional neural network to train 99 99 3 small patches, which were split from 3264 2248 road images collected by low-cost smartphones. The output of the network was the probability of whether a small patch was a crack or not. Their study shows that deep CNNs are superior to traditional machine learning techniques, such as SVM and boosting methods, in detecting pavement cracks. Pauly et al. [40] used a self-designed CNN model to study the relationship between network depth and network accuracy and proved the effectiveness of using a deeper network to improve detection accuracy in pavement crack detection based on computer vision. In contrast with [39], which used the same number of convolution kernels in all convolution layers, Nguyen et al. [41] used a convolution neural network with an increased number of convolution kernels in each layer because the features were more generic in the early layers and more original dataset specific in later layers [42]. Eisenbach et al. [43] presented the GAPs dataset, constructed a CNN network with eight convolution layers and three full connection layers, and analyzed the effectiveness of the state-of-the-art regularization techniques. However, its network input size was 64 64 pixels, which was too small to provide enough context information. The same problem also existed in [44,45,46].
Zhang et al. put forward CrackNet [52], which is an earlier study on pixel-level crack detection based on CNN. The prominent feature of CrackNet is using a CNN model without a pooling layer to retain the spatial resolution. Fei et al. have upgraded it to Cracknet-V [53]. While CrackNet and its series versions perform well, they are primarily used for 3D road crack images, and their performances on two-dimensional (2D) road crack images have not been validated. Fan et al. [3] proposed a pixel-level structured prediction method using CNN with full connections (FC) layers, but it has the disadvantage that it requires a long inference time for testing.
The CFD dataset, published in [23], consists of 118 RGB images with a resolution of 480 320 pixels. All of the images were taken using an iPhone5 smartphone on the road in Beijing, China, and can roughly reflect the existing urban road conditions in Beijing. These crack images have uneven illumination and contain noise such as shadows, oil spots, and lane lines, and most cracks in these images are thin cracks, which make crack detection difficult. We randomly divided 70% of the dataset (82 images) for training and 30% of the dataset (36 images) for testing.
The Crack500 dataset, shared by Yang et al. in the literature [60], contains 500 original images with a resolution of 2560 1440 collected at the main campus of Temple University. Each original image was cropped into a non-overlapping image area of 640 360, resulting in 1896 training images, 348 validation images, and 1123 test images. These images are characterized by low contrast between cracks and background, as well as noise such as oil pollution and occlusions, which increase the difficulty of detection.
The DeepCrack dataset [2] contains 537 crack images, including both concrete pavement and asphalt pavement, with complex background and various crack widths, ranging from 1 pixel to 180 pixels. We kept the same data split as the original paper, with 300 images for training and 237 images for testing.
Figure 3 shows the crack detection results of six typical input images of our method and the three methods to be compared. The first column is the original input crack image, the second column is the label image corresponding to the first column image, and the next four columns are the predicted output images of the four comparison algorithms. As can be seen from Figure 3, all these algorithms could detect the rough crack profile. However, in terms of details, all three algorithms, FCN, Unet, and SegNet, had false detection and missing cracks resulting in discontinuity of cracks to a varying degree. Our algorithm was obviously better than the three algorithms, with the least false detection and missing cracks, and the closest to the ground truth.
Graphene, due to its remarkable properties, is expected to be a key element for nanomachines and nanoelectromechanical systems (NEMs). Progressively, research on graphene should be shifted from property characterization towards reliability performance. In this context, an initial effort is made here to establish a diagnostic technique, capable of detecting the size and the position of a straight crack in graphene. The formulation is grounded on the fact that the presence of a crack in graphene has a significant impact on its vibration behavior. Not only the crack size but also the position of the crack influences the eigenbehavior of graphene. Hence, in the present study, the free vibration of a cracked and an uncracked graphene sheet of the same size is simulated to compute the first three natural frequencies as well as to calculate the corresponding frequency changes. The simulations are realized by adopting an efficient, atomistic, three-dimensional, spring-based, structural mechanics method. Numerous crack sizes and locations and two representative crack orientations are investigated. Validation of the adopted numerical approach is preceded through comparisons with relevant data found in the literature, regarding the free vibration of pristine graphene. Then, the diagrams of the arisen natural frequency shifts due to the crack are appropriately superposed in common contour maps to enable simple crack identification, i.e., detection of the crack length and position, from contour intersections.
Based on the quasi-conforming (QC) element technique, accurate and reliable eight-node and six-node solid-shell elements are presented in this paper. These QC solid-shell elements can alleviate shear and Poisson thickness locking by appropriately interpolating the strain fields over the element domain, and they are completely free from hourglass modes by ensuring the rank sufficiency of the element stiffness matrix a priori. Furthermore, the element stiffness matrices of the present elements are evaluated explicitly rather than resorting to the numerical integration, which leads to a high computational efficiency. The QC solid-shell elements with the properly interpolated element strain fields can rigorously pass both membrane and bending patch tests. The popular benchmark problems are used to evaluate the performance of the QC solid-shell elements. The numerical results show that the present QC solid-shell elements yield not only accurate displacements but also good stress results for all the stress components. Particularly, the present QC solid-shell elements are capable of giving quite accurate results even with very coarse mesh.
Interfacial behavior in the microstructure and the plastic deformation in the protein matrix influence the overall mechanical properties of biological hard tissues. A cohesive finite element model has been developed to investigate the inelastic mechanical properties of bone-like biocomposites consisting of hard mineral crystals embedded in soft biopolymer matrix. In this study, the complex interaction between plastic dissipation in the matrix and bonding properties of the interface between minerals and matrix is revealed, and the effect of such interaction on the toughening of bone-like biocomposites is identified. For the case of strong and intermediate interfaces, the toughness of biocomposites is controlled by the post yield behavior of biopolymer; the matrix with low strain hardening can undergo significant plastic deformation, thereby promoting enhanced fracture toughness of biocomposites. For the case of weak interfaces, the toughness of biocomposites is governed by the bonding property of the interface, and the post-yield behavior of biopolymer shows negligible effect on the toughness. The findings of this study help to direct the path for designing bioinspired materials with superior mechanical properties.