Highway tunnels operated in karst areas are prone to serious structural diseases such as water–mud inrush, lining tension and cracks, and road humps. This article reviews the historical diseases of the tunnel based on the Nanshibi Tunnel project. The cause analysis of the structural diseases of Nanshibi Tunnel was performed on the basis of a refined geophysical prospecting scheme for the complex karst geological structure environment. A series of comprehensive treatments were conducted to address the tunnel diseases in the karst area. The treatments included surrounding rock grouting, lining replacement, and adding inverted arches. The results show that the refined geophysical prospecting scheme and disease remediation idea for the complex karst geological structure environment proposed herein can realize the full coverage and accurate detection of karst structures, water passage, and tunnel-lining diseases. The proposed scheme can effectively solve the technical problems of karst tunnel diseases that occur repeatedly and are difficult to be completely rectified. Finally, specific engineering practice suggestions were proposed for tunnel disease remediation in the karst area.

Changes in lateral clearance are prone to drastic changes in the driving environment at the entrance and exit of the tunnel, which can cause a driver to become psychologically stressed and deviate from the center of a lane, thus creating a greater security risk. However, most of the existing regulations and studies only focus on the horizontal and vertical alignment of the tunnel entrances and exits, and there are few studies on the influence of lateral clearance on driving behavior. This study hired 15 random subjects to conduct real vehicle tests in eight tunnels on expressways with 3 design speeds by using a CAN-OBD analyzer and steering wheel angle meter. First, in five lateral clearance variation schemes, different speed characteristic indicators and steering wheel angles were selected as the indicators of driving behavior. Second, the interactive influence of the design speed, lateral clearance, operating speed, steering wheel angle, and other indicators were analyzed. Finally, paired t-test analysis and Wilcoxon and Friedman nonparametric tests were used to compare the differences in various indicators among different lateral clearance schemes. The results showed that when the left lateral clearance is 1.5 meters, the operating speed is increased by 3.9, while the standard deviation of speed is small, and the driving performance is higher. When the right lateral clearance is 1.75 and 2.00 meters, the operating speed is not much different. However, the latters speed standard deviation is smaller. By contrast, when the right lateral clearance is up to 2.25m, the operating speed increases by 3.7. However, the speed standard deviation also increases. Different lateral clearances have little effect on the steering wheel angle. The operating speed on the right side is higher and more stable when the design speed is 100km/h. This study provides scientific suggestions for the setting of the lateral clearance of the tunnels.

More complex geological conditions could be encountered with the construction of urban subway projects. At present, many subway tunnels have been built in composite strata with upper soft and lower hard layers, but the presence of a cavity in the strata increases the risk of collapse during construction. In this paper, a series of model experiments and discrete element methods were conducted to investigate the failure behavior of composite strata with a cavity caused by tunnel excavation disturbance. The influence of the distance between the cavity and vault (h_d) and the distance between the soil–rock interface and vault (h_r) on the collapse of the composite strata are analyzed. The research results indicate that tunnel collapse exhibits progressive failure because of the forming of a collapsed arch in the strata. If the h_d is greater than the tunnel span (D), the arch can be stabilized without other disturbances. Additionally, the thickness of the tunnel rock layer affects the height of the collapsed arch significantly, as it is difficult to form a stable arch when the h_r is less than 2/3 D. Finally, reasonable construction safety distances are proposed based on the possibility of forming a stable arch collapse in the tunnel and determining the range of the collapse.

This study focuses on monitoring the deformation of the shallow unsymmetrical section of a super-large-span tunnel portal relying on the newly built Shimentangshan Tunnel, and through numerical simulations, the construction sequence and drift ratios were optimized to address challenges related to the stability of surrounding rock and structure. The findings indicate that employing the double-side drift method results in a maximum settlement value of 107.0 mm and a maximum convergence value of 108.8 mm, leading to larger deformations. Excavating the shallow buried side first followed by the deep buried side proves beneficial for deformation control of the support structure and effectively limits damage to the surrounding rock. A drift ratio of 0.3 ensures optimal support structure security and stability. Considering both structural deformation and surrounding rock damage, a ratio between 0.25 and 0.35 for the drifts is recommended. Taking into account construction efficiency and economic benefits, a construction plan for the shallow buried unsymmetrical section at the portal of super-large-span tunnels is proposed.

Mobile inspections conducted by intelligent tunnel robots are instrumental in broadening the inspection reach, economizing on inspection expenditures, and augmenting the operational efficiency of inspections. Despite differences from fixed surveillance, mobile-captured traffic videos have complex backgrounds and device conditions that interfere with accurate traffic event identification, warranting more research. This paper proposes an improved algorithm based on YOLOv9 and DeepSORT for intelligent event detection in an edge computing mobile device using an intelligent tunnel robot. The enhancements comprise the integration of the Temporal Shift Module to boost temporal feature recognition and the establishment of logical rules for identifying diverse traffic incidents in mobile video imagery. Experimental results show that our fused algorithm achieves a 93.25% accuracy rate, an improvement of 1.75% over the baseline. The algorithm is also applicable to inspection vehicles, drones, and autonomous vehicles, effectively enhancing the detection of traffic events and improving traffic safety.

Mobile inspections conducted by intelligent tunnel robots are instrumental in broadening the inspection reach, economizing on inspection expenditures, and augmenting the operational efficiency of inspections. Despite differences from fixed surveillance, mobile-captured traffic videos have complex backgrounds and device conditions that interfere with accurate traffic event identification, warranting more research. This paper proposes an improved algorithm based on YOLOv9 and DeepSORT for intelligent event detection in edge computing mobile device using the intelligent tunnel robot. The enhancements comprise the integration of the Temporal Shift Module to boost temporal feature recognition and the establishment of logical rules for identifying diverse traffic incidents in mobile video imagery. Experimental results show that our fused algorithm achieves a 93.25% accuracy rate, an inprovement of 1.75% over the baseline. The algorithm is also applicable to inspection vehicles, drones, and autonomous vehicles, effectively enhancing the detection of traffic events and improving traffic safety.