Traditional manual inspection methods for tunnel lining leakage are subjective and inefficient, while existing models lack sufficient recognition accuracy in complex scenarios. An intelligent leakage identification model adaptable to complex backgrounds is therefore needed. To address these issues, a Vision Transformer (ViT) was integrated into the UNet architecture, forming an SE-TransUNet model by incorporating SE-Block modules at skip connections between the encoder-decoder and the ViT output. Using a hybrid leakage dataset partitioned by k-fold cross-validation, the roles of SE-Block and ViT modules were examined through ablation experiments, and the model’s attention mechanism for leakage features was analyzed via Score-CAM heatmaps. Results indicate: (1) SE-TransUNet achieved mean values of 0.8318 (IoU), 0.8304 (Dice), 0.9394 (Recall), 0.8480 (Precision), 0.9733 (AUC), 0.8562 (MCC), 0.9218 (F1-score), and 6.53 (FPS) on the hybrid dataset, demonstrating robust generalization in scenarios with dent shadows, stain interference, and faint leakage traces. (2) Ablation experiments confirmed both modules’ necessity: The baseline model’s IoU exceeded the variant without the SE module by 4.50% and the variant without both the SE and ViT modules by 7.04%. (3) Score-CAM heatmaps showed the SE module broadened the model’s attention coverage of leakage areas, enhanced feature continuity, and improved anti-interference capability in complex environments. This research may provide a reference for related fields.

Manual inspection methods traditionally used for tunnel lining leakage suffer from high subjectivity and low efficiency. At the same time, existing detection models do not perform well in terms of accuracy when faced with complex scenarios; this makes it essential to develop an intelligent leakage identification model that can adjust to varying background conditions. This study integrates a Vision Transformer (ViT) into UNet and constructs CBAM-TransUNet by embedding CBAMs into skip connections between the encoder-decoder structure and ViT outputs. Ablation experiments validate the efficacy of the CBAM and the ViT, while Score-CAM heatmaps analyze the model’s attention mechanism toward leakage features. The research results are as follows: (1) CBAM-TransUNet achieves average performance across metrics: 0.8143 (IoU), 0.8433 (Dice), 0.9518 (recall), 0.8482 (precision), 0.9837 (accuracy),0.9746 (AUC), 0.8568 (MCC), and 0.8970 (F1-score). These results indicate that the model performs excellently, even with dent shadows, stain interference, or faint traces. (2) Ablation experiments validate the pivotal roles of the CBAM and the ViT module: the IoU of the baseline model is 6.10% higher than that of the variant without the CBAM and 7.79% higher than that of the variant with both modules removed. (3) Through Score-CAM heatmap analysis, it is observed that the CBAM broadens the model’s attention coverage over leakage regions, strengthens feature continuity, and consequently enhances the model’s anti-interference performance in complex environments. This research could provide valuable reference insights for related fields.

Drill-and-blast tunnel construction continuously releases high-intensity dust during drilling, blasting, and shotcreting, while conventional forced ventilation is often insufficient to control dust migration and worker exposure. This study develops three-dimensional Euler–Lagrange gas–solid two-phase models for these three typical processes to clarify the spatiotemporal dispersion of polydisperse dust and to explore effective control strategies. The simulations show that all processes generate a persistent high-concentration dust belt near the tunnel face, and a low-velocity recirculation zone at the crown acts as a structural hotspot of dust accumulation that is difficult to purge by longitudinal ventilation. Particle size strongly affects dispersion behaviour: coarse particles rapidly settle near the source under gravity, whereas fine and medium-sized particles remain suspended for long periods and can be transported over long distances, particularly after blasting. Based on these findings, a rail-mounted purification system with a dynamically adjustable position along the tunnel is proposed, and its preferred deployment zones are determined to work synergistically with the main airflow. The system is designed to perform near-source and crown-targeted removal, providing an engineering-oriented “dynamic local purification plus overall ventilation dilution” pathway for improving air quality in drill-and-blast tunnel construction.

Sediment accumulation in the drainage systems of mountain tunnels is a typical issue threatening operational safety. To explore the sedimentation behavior under the coupling of multiple factors, this study systematically analyzes the coupled effects of sediment content, flow rate, slope, and cross-sectional shape on sedimentation through full-scale experiments and numerical simulations. The results indicate that: (1) the sediment accumulation is linearly positively correlated with sediment concentration (fitting slope of 0.87) and exponentially negatively correlated with flow rate and slope (R² > 0.90); (2) for drainage trenches with different cross-sectional shapes under the same boundary conditions, the maximum flow velocity and anti-sedimentation capacity rank as narrow rectangular > semi-circular ≈ inverted trapezoidal > rectangular; (3) the study proposes engineering anti-sedimentation strategies, such as moderately increasing the slope and adopting a periodic concentrated discharge model to enhance sediment transport capacity using peak flow; (4) under the premise of meeting drainage and flood control standards, the inverted trapezoidal or semi-circular cross-sections are preferred. The bottom waterway width can be reduced to increase flow velocity, thereby achieving a synergistic optimization of drainage efficiency and operational reliability. This provides a quantitative basis for the structural selection and anti-sedimentation design of tunnel drainage systems.

Tunnel drainage deterioration reduces groundwater release capacity and may increase local water pressure behind linings, whereas aggregate outlet discharge can conceal different local pressure states after capacity or connectivity changes. This study determines when aggregate discharge remains an informative proxy for local pressure and when that inference fails. A reduced framework coupling groundwater supply, finite component capacity, and network topology was evaluated using cross-head blockage-discharge data, a 6 × 6 blockage length–area dataset, and multi-outlet blockage cases. Increasing blockage from 60% to 80% reduced normalized discharge from 0.73–0.84 to 0.26–0.29. The excluded-head normalized-discharge root-mean-square error (RMSE) was 0.041, whereas the row-/column-level pressure RMSE was 0.242 kPa. The fitted area exponent (0.748) exceeded the length exponent (0.153). Two outlet-blockage layouts differed by only 0.019 in discharge ratio but by 30.6 kPa at the same invert location. Capacity loss governs overall drainage, whereas blockage position and active paths control local pressure allocation. The framework supports quasi-steady screening under measured or stable source and tailwater boundaries; when topology or boundaries may have changed, aggregate discharge should be supplemented by local pressure monitoring or geometry-specific analysis.