During the construction process, it is difficult to ensure the structural safety of shallow buried tunnel with the ultra-small clear distance since the tunnel is prone to instability and the surrounding rock and soil are in an adverse stress condition. To address this issue, a hybrid construction method is proposed to enhance tunnel stability and reinforce the surrounding rocks and soil. First, aiming at an actual tunnel, numerical analysis are provided to compare the effectiveness of different construction methods such as the bench method, advanced reinforcement method, and grouting reinforcement method. Second, the performance of the combination of advanced reinforcement and grouting reinforcement are discussed, and, on the basis of this discussion, the hybrid construction method, combining the advanced small pipes reinforcement, middle rock wall reinforcement, and grouting reinforcement, is proposed. And the characteristics of proposed method is compared with the traditional CRD construction method. The results reflect that using the hybrid construction method can enhance the stability of the tunnel and its effect is similar to that of the CRD method. Finally, the effectiveness of proposed hybrid construction method is verified by using the measured data obtained during the construction of an actual tunnel with the ultra-small clear distance. The results shown that the proposed method can enhance the stability of the tunnel and improve the bearing capacity of the surrounding rock and soil.
For the operational subway tunnel, the manual inspection accounts for the majority in terms of detecting the diseases and damage of tunnel. The accuracy of manual inspection mainly depends on the professional level of the detection personnel, and the whole detection process always is inefficient, which cannot meet the needs of actual tunnels. To address this issue, the intelligent mobile tunnel detection vehicle emerges as the times require. By using advanced technologies such as laser scanning and high-speed camera array, the subway tunnel detection vehicle has achieved the advantages of simple operation, comprehensive function and automatic detection. However, the current subway tunnel detection vehicle mainly realizes the scanning detection of tunnel surface diseases, and the detection of tunnel structural diseases is less involved. Based on the track and tunnel detection requirements, this study analyzes the current situation and existing problems of subway tunnel detection comprehensively, puts forward the development direction of tunnel structure detection, and the application prospect of intelligent detection vehicle in subway tunnel is prospected.
Fully distributed optical fiber sensing technology allows the high-density strain to measure the overall curvature and cross-section deformation of tunnels. However, there are few studies on the use of longitudinal strain along the tunnel to measure the cross-section convergence deformation, and the method of obtaining the strain along the tunnel loop is costly. To address this issue, a method of monitoring the cross-section deformation of tunnels using the strain data is proposed. First, a model of the relationship between strain and deformation in tunnels is constructed to obtain the overall settlement using the longitudinal strain. Second, based on the finite element method (FEM), the deformation law about the strain measured points and non-measured points on the cross-section of the tunnel is proposed, and on this basis, the correlation coefficient is presented. Using the product of overall settlement and correlation coefficient, the cross-section deformation at non-measured points is obtained. The results of numerical examples shown that the proposed method can effectively expand the monitoring scale and realize high-density cross-section deformation measurement of tunnels.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.