The number and scale of tunnels around the world are continuously increasing, but various disease problems during the operation period have also followed, and they have become one of the important problems facing tunnels at present. Many detection methods have been proposed in the field of tunnel detection, such as traditional manual detection method, ultrasonic detection method, ground-penetrating radar method, laser scanning method and inspection method based on image processing technology. However, due to the high cost of equipment, single test content, strict test environment and other reasons, most of the current tunnel routine inspection is still manual inspection. In order to solve the existing problems in tunnel detection, a method for rapid detection and treatment analysis of cracks in tunnel linings based on deep learning is proposed. Firstly, lining cracks were selected as the main research objects, and their causes and treatment measures in different parts were analyzed. Secondly, the AlexNet convolutional neural network based on the Caffe framework was used to identify the cracks. The crack images were collected to establish a data set, and the network parameters were modified and trained. Then use MATLAB to extract the crack length and width, and design a human-machine interactive tunnel lining crack detection program in MATLAB GUI. Finally, the content and results of this paper are discussed.
Digital image correlation (DIC) is a non-contact, full-field optical measurement method that has been extensively used in various applications like structural health monitoring, material characterization, high temperature testing etc. However, most of the above applications are used in the laboratory, and rarely employed in the actual structure. This is mainly because of, firstly, DIC instruments are expensive and cannot be afforded by ordinary people. Secondly, the corresponding equipment is complicated to operate and cannot be widely used. The built-in camera of the smartphone is becoming more and more high-definition so that it has brought about an opportunity to solve this problem. From this point of view, this study monitored the deformation of compressive concrete blocks by smartphone and DIC technology, and analyzed the changes of displacement and strain field on the surface of concrete blocks under different loads. The location and shape of cracks in the displacement results were compared with those in the actual image as well. The results show the feasibility of using smartphones to monitor the strain of structures.