Advanced Road Design Crack =LINK=

Advanced Road Design Crack =LINK=

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Abstract:The technological innovation of continuously reinforced concrete pavement (CRCP) that contains a significantly reduced amount of reinforcement and the same fundamental behavior as CRCP is called advanced reinforced concrete pavement (ARCP). This new concept of a rigid pavement structure is developed to eliminate unnecessary continuous longitudinal steel bars of CRCP by using partial length steel bars at predetermined crack locations. In Belgium, partial surface saw-cuts are used as the most effective crack induction method to eliminate the randomness in early-age crack patterns by inducing cracks at the predetermined locations of CRCP. The reinforcement layout of ARCP is designed based on the distribution of steel stress in continuous longitudinal steel bar in CRCP and the effectiveness of partial surface saw-cuts as a crack induction method. The 3D finite element (FE) model is developed to evaluate the behavior of ARCP with partial surface saw-cuts. The early-age crack characteristics in terms of crack initiation and crack propagation obtained from the FE simulation are validated with the field observations of cracking characteristics of the CRCP sections in Belgium. The finding indicates that there is fundamentally no difference in the steel stress distribution in the partial length steel bar of ARCP and continuous steel bar of CRCP. Moreover, ARCP exhibits the same cracking characteristics as CRCP even with a significantly reduced amount of continuous reinforcement.Keywords: early-age crack induction; partial surface saw-cuts; advanced reinforced concrete pavement; continuously reinforced concrete pavement; finite element simulation

Pavement crack detection plays an important role in the field of road distress evaluation [1]. Traditional crack detection methods depend mainly on manual work and are limited by the following: (i) they are time consuming and laborious; (ii) they rely entirely on human experience and judgment. Therefore, automatic crack detection is essential to detect and identify cracks on the road quickly and accurately [2]. This procedure is a key part of intelligent maintenance systems, to assist and evaluate the pavement distress quality where more continual road status surveys are required. Over the past decade, the development of high-speed mobile cameras and large-capacity hardware storage devices has made it easier to obtain large-scale road images. Through mobile surveying and mapping technology, integrated acquisition equipment is fixed to the rear of the vehicle roof frame to monitor both the road surface and the surrounding environment. The images can be acquired by processing and storing pavement surface images that are realized [3]. Currently, many methods utilize computer vision algorithms to process the collected pavement crack images and then obtain the final maintenance evaluation results [4].

The rest of this paper is organized as follows. Section 2 describes crack detection based on deep learning semantic segmentation. Section 3 demonstrates the effectiveness of the proposed scheme through comparative analyses of experiments. Section 4 discusses the detailed design of the two modules proposed in this paper. Finally, Section 5 concludes the paper.

Our CrackDataset consists of pavement detection images of 14 cities in the Liaoning Province, China. The data cover most of the pavement diseases in the whole road network. These images include collected images of different pavement, different illumination, and different sensors. The real values in the dataset provide two types of labels, cracks, and noncracks. The dataset is divided into three parts. The training set and the validation set are composed of 4736 and 1036 crack images, respectively. The test set contains 2416 images. In addition, two other crack datasets, CFD [15] and AigleRN [10], are used as test sets. The details of the datasets are shown in Table 2.

In this paper, an end-to-end trainable pavement crack detection framework based on DCNN, CrackSeg, is proposed, which can automatically detect road cracks under complex backgrounds. First, a crack training dataset is established, which covers a wide range of data sources and reflects the overall situation of pavement distress in the Liaoning Province, China. Second, through the fusion of high-level features in the backbone network, we propose the multiscale dilated convolution module. By capturing the features of context information at multiple scales, the crack detection network can learn rich semantic information in a complex background. Therefore, based on the dilated convolution theory, we design a novel network structure that can be inserted into the existing semantic segmentation system to improve the accuracy of crack feature detection. Finally, through the upsampling module, the low-level features, and continuous convolution features are fused to realize the crack pixel-level prediction. This feature aggregation, which combines different levels of feature information, can not only fully mine the crack features in the image but also restore and describe the details of the object boundary information. The experimental results of CrackSeg achieve high performance with a precision of 98.00%, recall of 97.85%, -score of 97.92%, and a mIoU of 73.53%, which are higher than those of other networks. Furthermore, the model has strong stability and robustness to solve the noise interference caused by shadows, stains, and exposures in the process of data acquisition. The good performance of the CrackSeg network provides a possibility for large area automatic crack detection.

Based on the traditional image processing method, it is the initial attempt to automatically detect road cracks. Akagic et al. [3] proposed a crack image detection method based on the Otsu threshold and histogram. Although this method is efficient, the crack area can be accurately found only when the crack pixel is darker than the surrounding pixels. Medina et al. [4] used the wavelet transform method to detect cracks, which not only was susceptible to the contrast between crack pixels and surrounding pixels, but also could not detect cracks with poor continuity. To improve the effect of detecting continuous cracks, the minimum path selection method [5] is proposed to detect cracks from a global perspective, which effectively enhances the continuity of fractured cracks. Although the minimum path selection method performs crack detection from a global perspective, its detection performance is still unsatisfactory when dealing with cracks with disordered shapes or low contrast with surrounding pixels. It can be seen that automatic detection of road cracks is still a difficult task for researchers.

In recent years, deep learning has been applied to road crack detection tasks due to its outstanding feature extraction capabilities. Pauly et al. [6] cropped each crack image into a patch, and then the patch was classified as crack or noncrack after neural network training. Although this method was very efficient, it produced false detections. To further improve its detection accuracy, semantic segmentation algorithms based on the encoding-decoding architecture are widely used. Lau et al. [7] introduced U-Net to road crack detection. The network introduced skip connections into the encoding-decoding architecture, which helped to preserve rich image details, thereby improving the detection accuracy. Although U-Net performs well in the field of image segmentation, the crack area of the crack image is much smaller than the background area. Cao et al. [8] replaced the U-Net encoder with ResNet34 to deal with the loss of spatial information caused by continuous pooling. Effectively avoiding gradient disappearance or gradient explosion, Chen et al. [9] embedded a global context module in the U-Net network structure to give the network the ability to capture global context information, which is conducive to the detailed segmentation of pavement crack images. Augustauskas and Lipnickas [10] introduced a kind of attention based on the U-shaped network. The force gate model suppresses background noise and strengthens the ability of the network to capture detailed features of cracks. Fan et al. [11] proposed an end-to-end pixel-level road crack detection network. By building multiple expansion convolution modules to help the network obtain the multiscale context information of the cracks, a hierarchical feature learning module is designed to integrate low-level features and high-level features. The designed multiscale output feature map has better performance in fracture information inference, thereby improving the robustness and universality of the network. Ali et al. [12] implemented a deep fully convolutional neural network based on residual blocks. For the extreme imbalance between target and background pixels in crack images, a local weighting factor was proposed to effectively reduce the trouble caused by pixel imbalance to the network; a crack image dataset with different crack width directions and a location dataset were developed for researchers to use for training, validation, and testing. Fan et al. [13] proposed a road crack automatic detection and measurement network based on probability fusion. Through the designed integrated neural network model, satisfactory crack detection accuracy is obtained; according to the predicted crack map, the width and length of the crack can be measured effectively. Wang et al. [14] proposed a semisupervised semantic segmentation network for crack detection. The model extracts multiscale crack feature information through Efficient-UNet; it greatly reduces the workload of labeling while maintaining high labeling accuracy. Wang et al. [15] used a neural network to detect pavement cracks and applied a principal component analysis to classify the detected pavement cracks. The crack types were divided into transversal, longitudinal, cracked cracks. The accuracy scored higher than 90%. Nevertheless, patch classification is only suitable for rougher classification tasks. Cubero-Fernandez et al. [16] classified the discontinuous cracks in an image as a whole, though they did not consider the spatial distribution relationship between the cracks. 2b1af7f3a8