In recent years, underwater shield tunnels are being developed according to large-scale sections. The problems of large buried depth and high water pressure have posed major challenges to the safety of segmented structures. The load-bearing capacity and damage of segmented structures under high water pressure features have always attracted attention. Based on a machine learning approach to smart grid energy management, this paper proposes a design method for high voltage tunnels in a balanced groundwater environment and tests the capacity of the high voltage tunnels. Based on the high water pressure failure test phenomenon of the large-section shield tunnel of the GIL project, this paper analyzes the failure characteristics and laws of the segment structure under high water pressure conditions. On this basis, an evaluation index for the load-bearing performance of the segment structure is proposed, and control suggestions are given based on the research results. According to the fault characteristics and the section structure law, the section performance evaluation index is proposed, and the control parameter recommendations are given based on the test results. Valuable discoveries and breakthroughs have been made in the failure of the prototype segment structure and the difference in the mechanical properties of the segment structure in the form of the high water pressure tunnel assembly. The research results show that under the condition of staggered assembly of high-voltage tunnels, the maximum dislocation amount of the high-voltage tunnel structure during instability failure is 10 mm, and the bolt strength is improved. The more important aspect is the existence of concave and tenon between the rings. In structure, the maximum stress of the bolts between the rings is only 38.6% of the yield stress at the time of instability failure. This indicates that the distributed concave-convex tenon between the segments not only can control the dislocation of the segments but also can ensure that the longitudinal bolts are well protected. It is safe to ensure the pressure resistance of the high water pressure tunnel.

In recent years, with the rapid development of tunnel construction in China, the length of tunnels has continued to increase, and the consequent tunnel disease detection has attracted more and more attention from maintenance departments. Among many diseases, lining cracks are the most common, which directly reflect the stress of the lining, which is very important for the study of tunnel diseases. In view of the current detection status and detection requirements, this article has carried out research work on a vehicle-mounted tunnel lining crack detection system based on image processing. Due to the grayscale difference between the cracks on the lining surface and the lining background, these differences lead to significant crack edge features and relatively stable detection. Therefore, this article designs an intelligent edge algorithm system for cracks on the lining surface to detect the edges of the image, extract the edges of cracks, and remove useless interference information in the lining background. The experiment proves that the paired sample t-test can find that after the experiment is over, the P value of different edge detection operators for global threshold segmentation is less than 0.05, which has a significant difference. The Canny, Deriche, and Lanser filters are relatively strong, and the extracted crack edge noise is relatively small. Finally, the parameter values of the crack image are calculated, and the calculated values of the crack parameters provide a scientific and reliable basis for tunnel safety evaluation.