Tunnel lining crack is the most common disease and also the manifestation of other diseases, which widely exists in plain concrete lining structure. Proper evaluation and classification of engineering conditions directly relate to operation safety. Particle flow code (PFC) calculation software is applied in this study, and the simulation reliability is verified by using the laboratory axial compression test and 1:10 model experiment to calibrate the calculation parameters. Parameter analysis is carried out focusing on the load parameters, structural parameters, dimension, and direction which affect the crack diseases. Based on that, an evaluation index system represented by tunnel buried depth (H), crack position (P), crack length (L), crack width (W), crack depth (D), and crack direction (A) is put forward. The training data of the back propagation (BP) neural network which takes load-bearing safety and crack stability as the evaluation criteria are obtained. An expert system is introduced into the BP neural network for correction of prediction results, realizing classified dynamic optimization of complex engineering conditions. The results of this study can be used to judge the safety state of cracked lining structure and provide guidance to the prevention and control of crack diseases, which is significant to ensure the safety of tunnel operation.

In this study, a tunnel model with a length of 20 m, a width of 5 m, and a height of 5 m was used, and an experimental investigation was conducted to examine the impact of high-pressure water mist on the temperature distribution along the tunnel ceiling. Specifically, different experimental settings, such as various nozzle pressures, nozzle positions, and longitudinal ventilation speeds, in the high-pressure water mist system were employed to investigate the smoke-spreading process of tunnel fire under different conditions, and an effective method utilizing a high-pressure water mist system was proposed for blocking smoke and heat. The experimental results reveal that the high-pressure water mist system can be used to effectively improve the ceiling temperature during tunnel fires; when the nozzle pressure is set as 10 MPa, and the nozzle position is located at x7, the highest thermal insulation efficiency in the tunnel is obtained. Additionally, the joint application of the high-pressure water mist system and the mechanical smoke exhaust effectively mitigates the ambient temperature within the tunnel, thereby playing a pivotal role in enhancing the fire safety of the tunnel.

To address the challenges of high algorithmic complexity and low accuracy in current fire detection algorithms for highway tunnel scenarios, this paper proposes a lightweight tunnel fire detection algorithm, FIRE-YOLOv8s. First, a novel feature extraction module, P-C2f, is designed using partial convolution (PConv). By dynamically determining the convolution kernel’s range of action, the module significantly reduces the model’s computational load and parameter count. Additionally, the ADown module is introduced for downsampling, employing a lightweight and branching design to minimize computational requirements while preserving essential feature information. Secondly, the neck feature fusion network is redesigned using a lightweight CNN-based cross-scale fusion module (CCFF). This module leverages lightweight convolution operations to achieve efficient cross-scale feature fusion, further reducing model complexity and enhancing the fusion efficiency of multi-scale features. Finally, the dynamic head detection head is introduced, incorporating multiple self-attention mechanisms to better capture key information in complex scenes. This improvement enhances the model’s accuracy and robustness in detecting fire targets under challenging conditions. Experimental results on the self-constructed tunnel fire dataset demonstrate that, compared to the baseline model YOLOv8s, FIRE-YOLOv8s reduces the computational load by 47.2%, decreases the number of parameters by 52.2%, and reduces the model size to 50% of the original, while achieving a 4.8% improvement in accuracy and a 1.7% increase in mAP@0.5. Furthermore, deployment experiments on a tunnel emergency firefighting robot platform validate the algorithm’s practical applicability, confirming its effectiveness in real-world scenarios.

Single image dehazing has been a challenging problem due to its ill-posed nature. This paper proposed a fast image dehazing algorithm for highway tunnel based on artificial multi-exposure image fusion. The original blurred images are transformed into under-exposed images using a series of gamma-correction operations. Meanwhile, a multi-exposure image fusion algorithm based on two-scale decomposition is proposed. It exploits the guided filter to separate each multi-exposure image into base and detail layers. The fused base and detail layers are integrated into the fused images. Then, a linear saturation adjustment method is utilized to enhance the saturation in spatial domain. Through the above image processing steps, the final haze-free images can be obtained. In this paper, the superiority of the proposed image dehazing algorithm based on artificial multi-exposure image fusion is demonstrated through theoretical analysis and experimental verification. It can quickly improve the visibility of foggy images, and make the processed images colorful and clear.

Lattices have attracted increasing attention for their outstanding mechanical and multifunctional properties. In this study, a novel class of hybrid lattices composed of Primitive (P) and I-Wrapped Package (W) topologies is proposed by a mathematical formula. The deformation behaviors, mechanical properties, and energy absorption characteristics of the hybrid lattices are systematically investigated using compression experiments and simulations. The results show that the hybrid lattices exhibit a localized initial failure followed by stress redistribution, effectively avoiding brittle interlayer collapse of the P-type sub-lattices and maintaining a high load-bearing capacity even after the initial failure. A synergistic enhancement effect of ‘1 + 1 > 2’ is observed, in which the hybrid lattices outperform the linear combination of their constituent sub-lattices. Compared with the total performance of the P and W sub-lattices, the hybrid lattices exhibit increases of 11.6%, 30.0%, 34.5%, and 368% in elastic modulus, yield strength, compressive strength, and energy absorption, respectively. The exceptional energy absorption capability of hybrid lattices is attributed to the synergistic deformations and stress redistribution mechanisms during the compression. The proposed hybrid lattices significantly improve energy absorption, and they have potential applications in a tunnel lamp maintenance robot.