Automatic Identification and Location of Tunnel Lining Cracks

The lining crack is common for the tunnel in the stage of operation, which has seriously influenced the service life and safety of tunnel engineering. It is a new trend to use computer vision to detect tunnel cracks over the past few years in China and foreign countries. By image processing technology and intelligent algorithm, the computer has a hominine visual perception system which understands, analyzes, and determines input image information, thus recognizing and detecting specific objectives. However, the effect of image recognition for tunnel crack now cannot satisfy the demands of practical engineering. SSD algorithm has been used when analyzing features of lining surface image, while comparison analysis has been made from image recognition results, error rate, and running time. The results indicate that the SSD algorithm can accurately and rapidly detect and mark the position of the tunnel crack. The tunnel information obtained from image recognition is subsequently imported into the team independently developed software GeoSMA-3D, which is useful for determining tunnel grade.

Intelligent crack damage detection system in shield tunnel using combination of retinanet and optimal adaptive selection

With the large-scale construction of urban subways, the detection of tunnel cracks becomes particularly important. Due to the complexity of the tunnel environment, it is difficult for traditional tunnel crack detection algorithms to detect and segment such cracks quickly and accurately. The article presents an optimal adaptive selection model (RetinaNet-AOS) based on deep learning RetinaNet for semantic segmentation on tunnel crack images quickly and accurately. The algorithm uses the ROI merge mask to obtain a minimum detection area of the crack in the field of view. A scorer is designed to measure the effect of ROI region segmentation to achieve optimal results, and further optimized with a multi-dimensional classifier. The algorithm is compared with the standard detection based on RetinaNet algorithm with an optimal adaptive selection based on RetinaNet algorithm for different crack types. The results show that our crack detection algorithm not only addresses interference due to mash cracks, slender cracks, and water stains but also the false detection rate decreases from 25.5–35.5% to about 3.6%. Meanwhile, the experimental results focus on the execution time to be calculated on the algorithm, FCN, PSPNet, UNet. The algorithm gives better performance in terms of time complexity.

Analysis and Study on Crack Characteristics of Highway Tunnel Lining

Lining cracks are one of the most common diseases in highway tunnels, and the existence of lining cracks directly affects the overall stability and durability of tunnels, which has an important impact on the safe operation of tunnels, and it is necessary to analyze and study the characteristics of tunnel lining cracks. Combining with the detection data of multiple highway tunnels in the field, the different types of tunnel cracks are divided, and the classification numerical statistics method is used to obtain that the number and length of annular cracks in highway tunnel cracks are significantly higher than those of the other two kinds of cracks, and the longitudinal cracks in tunnel crack cracking degree are greater than the circumferential cracks and the inclinded cracks. The influence degree of cracks on the safety of tunnel structure longitudinal cracks are relatively the largest, the inclinded cracks are second only to longitudinal cracks, and the influence of cyclic cracks is relatively small. It provides reference for tunnel engineering design, construction, operation management and comprehensive improvement work.