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.

Traditional methods for predicting surrounding rock grades ahead of tunnel faces encounter challenges: image-based approaches are susceptible to environmental interference, while parameter-based classification may disrupt construction. This study proposes an intelligent rock grade identification method by integrating longitudinal displacement profile (LDP) evolution patterns with deep learning. First, the numerical model was validated against V-D theoretical curves, and LDP evolution laws were systematically analyzed for three rock types (GSI = 15, 30, 50) under nine geological combinations. The results indicate that (1) homogeneous strata exhibit deformation peaks followed by declines; (2) GSI = 15 strata show significantly larger deformations; and (3) stratified schemes display pre-interface deformation peaks and post-interface deformation controlled by subsequent lithology. A novel hybrid neural network was developed to classify strata using LDP curves as input. The model achieved 93.25% training accuracy and 91.20% validation accuracy. Ablation experiments demonstrated their superiority over the other four models with partial module deletions, achieving improvements in test accuracy of 3.24%, 3.08%, 4.16%, and 6.48%, respectively, compared to those models. This lightweight solution effectively overcomes the limitations of manual expertise dependency in conventional models and environmental sensitivity in visual methods. By synergizing LDP evolution analysis with deep learning, this framework provides a reliable approach for real-time rock grade prediction during tunnel advancement.

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.

The compactness of the secondary lining in tunnels is crucial for the overall stability and durability of the structures. This study aims to thoroughly analyze the causes and formation mechanisms of voids in the secondary lining, providing scientific guidance for construction and monitoring in tunnel engineering. Firstly, through field investigations and theoretical analysis, key factors leading to the formation of voids in the secondary lining were identified, and a corresponding formation mechanism model was established. The risk of voiding at various positions of the vault was assessed, and risk levels were categorized. Secondly, a novel real-time monitoring system was developed to capture internal void changes within the lining accurately, enabling real-time monitoring of void phenomena. Furthermore, based on a deep understanding of the void formation mechanisms, the monitoring scheme was optimally designed. Field applications verified the effectiveness of the monitoring system and the feasibility of the optimized monitoring scheme. The results indicate that this real-time monitoring system significantly improves the detection accuracy of voids in tunnel linings. The optimized monitoring scheme effectively guides construction, reducing the occurrence of voids and thereby enhancing the quality and safety of tunnel engineering. This study not only provides new technical means for the construction and monitoring of secondary tunnel linings but also offers theoretical references and practical guidance for solving similar engineering problems.

Voids behind tunnel linings are common hidden defects in underground engineering, leading to reduced structural capacity and potential safety hazards. To address the deficiencies in the understanding of the mechanism and the optimization of design of the existing steel plate reinforcement methods, this study systematically investigates the reinforcement mechanisms and proposes refined design strategies through numerical simulations and experimental validation. First, a comparative analysis of the Concrete Damage Plasticity (CDP) model and the Extended Finite Element Method (XFEM) revealed that the CDP model exhibits superior accuracy and computational efficiency in simulating large-scale void linings. Second, the effectiveness of different reinforcement schemes (chemical anchor bolts alone, structural adhesive alone, and combined systems) was evaluated, demonstrating that structural adhesive dominates stress transfer, while chemical anchor bolts primarily prevent plate detachment. Through further optimization simulations of the steel plate spacing, it was found that a spacing of 0.25 m can balance the reinforcement effect and cost. This spacing restricts the maximum principal stress (1.83 MPa) below the tensile strength of concrete while essentially eliminating damage to the lower surface of the lining. An optimized steel plate reinforcement structure was ultimately proposed. By reducing the number of chemical anchor bolts and decreasing their size (with only M12 chemical anchor bolts arranged at the edges), local damage is minimized while maintaining reinforcement efficiency. The research results provide theoretical support and engineering guidance for the safe repair of tunnel void areas.