Accurate detection of asphalt pavement distress is crucial for road maintenance and traffic safety. However, traditional convolutional neural networks usually struggle with this task due to the varied distress patterns and complex background in the images. To enhance the accuracy of asphalt pavement distress identification across various scenarios, this paper introduces an improved model named SMG-YOLOv8, based on the YOLOv8s framework. This model integrates the space-to-depth module and the multi-scale convolutional attention mechanism, while optimizing the backbone’s C2f structure with a more efficient G-GhostC2f structure. Experimental results demonstrate that SMG-YOLOv8 outperforms the YOLOv8s baseline model, achieving P_macro and mAP50 scores of 81.1% and 79.4% respectively, marking an increase of 8.2% and 12.5% over the baseline. Furthermore, SMG-YOLOv8 exhibits clear advantages in identifying various types of pavement distresses, including longitudinal cracks, transverse cracks, mesh cracks, and potholes, when compared to YOLOv5n, YOLOv5s, YOLOv6s, YOLOv8n, and SSD models. This enhancement optimizes the network structure, reducing the number of parameters while maintaining excellent detection performance. In real-world scenarios, the SMG-YOLOv8 model, when applied to image data collected from projects, achieves a P_macro of 80.5% and an R_macro of 86.2%. This result demonstrates its excellent generalization capability and practicality. The model provides significant technical support for the intelligent detection of pavement distress.

Soil water content is one of the most important factors affecting the safety and stability of buildings or structures, especially in roadbeds, slopes, earth dams and foundations. Accurate assessments of soil water content can ensure the quality of construction, reduce construction costs and prevent accidents, among other benefits. In this study, ground penetrating radar (GPR) was used to detect and evaluate changes in soil water content. The GPR signal is usually nonstationary and nonlinear; however, traditional Fourier theory is typically suitable for periodic stationary signals, and cannot reflect the law of the frequency and energy of the GPR signal changing with time. Wavelet transform has good time-frequency localization characteristics, and therefore represents a new method for analyzing and processing GPR signals. According to the time-frequency characteristics of GPR signals, in this paper, a new biorthogonal wavelet basis which was highly matched with the GPR waveform was constructed using the lifting framework of wavelet theory. Subsequently, an evaluation method, namely, the wavelet packet-based energy analysis (WPEA) method, was proposed. The method was utilized to calculate the wavelet packet-based energy indexes (WPEI) of the GPR single-channel signals for clay samples with water contents ranging from 10% to 24%. The research results showed that there was a highly correlated linear relationship between the WPEI and the soil water contents, and the relationship between the two was fitted with a linear fitting function. The feasibility of the method was verified by comparing our results with those obtained using classical wavelet bases to perform the wavelet packet transform. The large-area, continuous scanning measurement method of GPR was shown to be suitable for evaluations of soil water contents in roadbeds, slopes, earth dams, and foundations.

Advanced geological prediction of tunnels has become an indispensable task to ensure the safety and effectiveness of tunnel construction before excavation in karst areas. Geological disasters caused by unfavorable geological conditions, such as karst caves, faults, and broken zones ahead of a tunnel face, are highly sudden and destructive. Determining how to predict the spatial location and geometric size of unfavorable geological bodies accurately is a challenging problem. In order to facilitate a three-dimensional quantitative analysis of the filler material ahead of the tunnel face, a biorthogonal wavelet with short support, linear phase, and highly matching waveform of ground penetrating radar (GPR) wavelet is constructed by lifting a simple and general initial filter on the basis of lifting wavelet theory. A method for a time-energy density analysis of wavelet transforms (TEDAWT) is proposed in accordance with the biorthogonal wavelet. Fifteen longitudinal and horizontal survey lines are used to detect void fillers of different heights. Then, static correction, DC bias, gain, band-pass filtering, and offset processing are performed in the original GPR profile to enhance reflected signals and converge diffraction signals. A slice map of GPR profile is generated in accordance with the relative position of longitudinal and horizontal survey lines in space. The wavelet transform analysis of a single-channel signal of each survey line is performed by adopting the TEDAWT method because of the similar rule of the single-channel signal of GPR on the waveform overlay and the ability of the constructed wavelet basis to highlight the time-frequency characteristics of GPR signals. The characteristic value points of the first and second interfaces of the void fillers can be clearly determined, and the three-dimensional spatial position and geometric sizes of different void fillers can be obtained. Therefore, the three-dimensional visualization of GPR data is realized. Results show that the TEDAWT method has a good practical application effect in the quantitative identification of void fillers, which provides a basis for the interpretation of advanced geological prediction data of tunnels and for the construction decision.

Blasting‐induced vibration during the excavation of transition section in a branching‐out tunnel causes damage and hence affects the safety and stability of the supporting structure and surrounding rock. To examine the effects of excavation and blasting of the transition section in the posterior tunnel on the supporting structures of the anterior tunnel, the influences of the blasting‐induced vibration in the posterior tunnel on the anterior tunnel were analyzed under different surrounding rock levels, excavation techniques, distances from explosive source, and net spans. This method was performed by combining numerical simulation with blasting‐induced vibration monitoring according to the construction characteristics of the transition section in a branching‐out tunnel of a highway. A control technique was investigated to assure the safety and stability of the anterior tunnel during the excavation and blasting of the posterior tunnel. Results demonstrate that (1) the vibration velocity peak behind the blasting excavation surface of the tunnel is higher than that in front. These results suggest paying much attention in monitoring vibration velocity within 10 m behind the excavation surface. (2) The blasting‐induced vibration velocity peak on the spandrel at the side that faces the blasting in the anterior tunnel is 2.0–2.5 times than that at the side behind the blasting. Moreover, the blasting‐induced vibration velocity peak on the haunch at the side that faces the blasting in the anterior tunnel is 6‐7 times than that at the side behind the blasting. (3) Instead of the full‐face excavation method, the use of center cross diagram (CRD) technique or side wall pilot tunnel method is suggested for the excavation of surrounding rocks of IV‐level, V‐level, and III‐level with a net span smaller than 3 m. (4) Vibration control measures, such as double wedge‐shaped cut blasting and floor blast‐hole staged detonation, were adopted by designing and optimizing blasting parameters (e.g., total explosives, maximal segment explosive quantity, detonation order, and detonation interval) in posterior tunnel. According to the test, the blasting‐induced vibration velocity peak, which is monitored in the anterior tunnel, can be controlled within 10 cm/s to assure the safety and stability of the supporting structure and surrounding rocks of the tunnel.

Geological investigation and surrounding rock classification ahead of tunnel faces are among the most critical fundamental tasks in tunnel construction. However, current methods for obtaining rock mass parameters and conducting grade evaluation are plagued by significant subjectivity, prolonged cycles, and insufficient intelligence. To address these issues, this study takes the Shitang Tunnel, Zaoxi Tunnel, Jiangjunshan Tunnel, Kuiba Tunnel, Dapingwei Tunnel as engineering cases, integrating geological survey data from tunnel faces and ground penetrating radar (GPR) advance detection data. A dynamic rock mass classification method during tunnel construction is then proposed based on deep learning and digital image processing techniques. The proposed method employs MobileNetV3 to intelligently identify key features in tunnel face images, including rock lithology, rock mass structure type, fracture development degree, weathering degree, and water content, achieving an identification accuracy exceeding 95%. Additionally, MobileNet-single shot multibox detector is utilized for rapid intelligent interpretation of GPR images, with an identification precision of 96.57%. By inputting the recognition results into the basic quality classification formula, real-time dynamic determination of rock mass grades is achieved, yielding a prediction accuracy of 85.7%. The results demonstrate that this method significantly enhances the intelligence level and operational efficiency of tunnel rock mass classification, providing a novel technical approach for dynamic design and construction optimization in tunnel engineering.