Sheng Zhang's research while affiliated with Hunan University and other places

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 backgrounds 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 Pmacro and mAP@50 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, and YOLOv8n 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 has demonstrated strong generalization capability and practical utility, providing crucial technical support for intelligent pavement distress detection.

Timely and accurate detection and identification of cavities under urban roads is the key to road safety. Due to the inability to obtain accurate migration velocity and the difficulty of achieving complete convergence of diffraction signals of the cavity disease, traditional migration methods struggle to accurately identify and locate the subgrade cavity. This paper proposes a GPR image migration processing (TUFK method) based on 2D undecimated wavelet transform and the F–K method in accordance with the high-precision imaging of the subgrade cavity. The finite-difference forward models of subgrade cavity without and with noise are established, and the model test of cavity detection by GPR is carried out in the laboratory. Through the fine extraction and migration processing of the weak diffraction signals from the cavity, the optimal velocity required for migration is analyzed, and the TUFK method is applied to the migration process of GPR data acquired for the purpose of cavity detection. Furthermore, the proposed method is applied to the processing of GPR data acquired in the field with a cavity below the roadbed. The results show that the TUFK method can accurately extract the diffraction signals from the cavity and achieve the fine convergence of cavity diffraction signals whether in noiseless or noisy environments. Compared with the traditional Kirchhoff and F–K migration methods, this method can effectively obtain accurate migration velocity and the migration results can reflect the actual position and shape of the cavity. This study can provide a new idea and effective method for the imaging of subgrade cavity.

The hard rocks in the stratum can pose safety risks and hinder the progress of urban underground tunnel construction using shield and jacking methods, thereby reducing construction efficiency and increasing construction costs. This paper utilizes wavelet scale energy spectrum, wavelet packet theory and statistical methods to conduct research on the detection of special geological formations such as hard rocks and voids, as well as the analysis of their signal time-frequency characteristics based on the ground-penetrating radar (GPR) technique. On the basis of calibrating the permittivity of different types of rock blocks, we established a forward model for detecting hard rocks and voids, and the simulated signals were analyzed in the time and frequency domains. Subsequently, laboratory experiments were conducted to perform GPR tests on different types of hard rocks in natural and water-saturated states and voids, to explore the time-frequency characteristics, frequency band energy variations, and statistical patterns of typical single-trace signals. The results show that the granite detection signal contains more low-frequency components, the sandstone detection signal contains more medium-low frequency components, while the limestone detection signal contains more medium-high frequency components in their natural state; the signal from the karst cave has relatively more low-frequency components than the signal from the empty cavity. The geometric shape of the rock has no influence on the dominant frequency and time-frequency distribution of its reflection signal. Generally, rocks with higher rebound values (hardness) also exhibit larger variance and standard deviation in frequency band energy. The research has important theoretical significance and practical value for the measurement and assessment of special geological features such as hard rocks and voids in urban underground trenchless construction.

Natural materials mostly consist of porous mixed media, the relative dielectric constant (RDC) of which is a variable. The RDC of measured materials is often simply estimated as a constant value for the purposes of ground-penetrating radar (GPR) detection, which is one of the reasons for the significant quantitative identification error of GPR results. Only by accurately measuring the RDC of porous mixed media to calibrate the electromagnetic wave velocity can accurate quantitative GPR identification results be obtained. However, the difficulty is how precisely to identify the feature points of GPR signal of the measured material. In this study, a model test was carried out in a laboratory setting on porous mixed media consisting of dry sand, dry loam, and limestone. A new method was then proposed to accurately measure the RDC of porous mixed media. On this basis, three stages of accurate measurements of GPR were proposed, namely, signal processing, wavelet analysis, and quantitative identification. Experimental results showed that the relative error rate between the measured value (5.555) and the theoretical value (7.250) of porous mixed media was 23.38%, and the relative error rate of the measured value obtained by the new method was reduced from 13.78% to 1.50% in respect of quantitative recognition of GPR. The proposed method offers both unique advantages in improving the detection accuracy of GPR and great market potential, especially for projects requiring the accuracy of non-destructive testing (NDT). In addition, this study also proposed potential methodological feasibility and research ideas for predicting the physical properties of measured materials by establishing sensitive regions within three-dimensional spaces in the future.

The analysis of time-frequency variation and energy refinement characteristics of blasting vibration signals has contributed to understanding the propagation law of blasting vibration wave and reducing the possible losses. Combined with the measured data of tunnel blasting excavation and based on the newly constructed wavelet function, the spectrum distribution and energy refinement characteristics of tunnel blasting vibration signals are deeply explored and studied. The results demonstrated that compared to the Fourier spectrum, the innovative method of scale energy spectrum can not only acquire the dominant frequency of the blasting vibration signals, but also the obtained spectrum curve is smoother and can clearly reflect the change trend of the signal spectrum. The newly constructed biorthogonal wavelet has the characteristics of high vanishing moment, high regularity and matching with the waveform variation of the measured blasting vibration signals, and can describe the subtle variation characteristics of blasting vibration signal frequency. The continuous wavelet transform energy spectrum can reflect the three-dimensional energy distribution of blasting vibration signal in the time-scale domain, and the occurrence time of frequency, the frequency duration interval and time range of blasting vibration signal can also be acquired. Wavelet packet algorithm can precisely calculate the energy distribution of each frequency component in the signal, the tunnel blasting vibration signals (YBJ1, YBJ2) generated near the power tower presents low frequency, while the signals (YBF3,YBF4) far away from the power tower presents relatively high frequency. Measures should be taken to control the vibration and resonance of power tower caused by tunnel blasting. This research is of great significance for recognizing the propagation law of vibration waves, reducing the impact of blasting on surrounding buildings, and ensuring the safety of tunnel construction and surrounding buildings.

The Fisher–Yates random shuffling (FRS) algorithm combined with the finite‐difference time‐domain (FRS‐finite‐difference time‐domain (FDTD)) method is proposed to construct a fine grid model for the forward simulation of ground‐penetrating radar (GPR) in mixed media. First, the FDTD method was used to divide the coarse grid model into several fine grid models by conforming to the boundary conditions of different media, and the corresponding dielectric parameters were assigned to Yee cells in each fine grid model. Then, the FRS algorithm was used to scramble all Yee cells with equal probability randomly, and the array of scrambled Yee cells was recombined into a coarse grid model. Finally, the geoelectric model of mixed media was generated with the FDTD method and a GPR image excited by electromagnetic wave pulses was obtained. To explore the characteristic signals and dielectric properties of the GPR electromagnetic response in mixed media, the image entropy theory was used to describe the GPR image, and the waveform analysis and wavelet transform mode maximum (WTMM) methods were used to analyze the single‐channel GPR signal of the mixed media. The results showed that the FRS‐FDTD method can be used to construct a valid and stable fine grid model for simulating GPR in mixed media. The model effectively inhibits electromagnetic attenuation and energy dissipation, and the WTMM method explains the relative dielectric permittivity distribution of the mixed media. The findings of this study can be used as a theoretical basis for correcting radar parameters and interpreting images when GPR is applied to mixed media. This article is protected by copyright. All rights reserved

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.