Yunhui Huang’s research while affiliated with South China University of Technology and other places

Embankment–bridge transition sections (EBTSs) suffer from diverse engineering diseases that have escalated into one of the most severe issues along the Qinghai‐Tibet Railway (QTR). Nevertheless, the causes and mechanisms of engineering diseases in EBTSs remain limited. This study employed a methodological approach to conduct field surveys in the Tuotuo River Basin in the hinterland of the Qinghai‐Tibet Plateau (QTP). Borehole investigations and nuclear magnetic resonance (NMR) techniques accurately determined the permafrost characteristics, enabling the correction of electromagnetic wave velocity and acquisition of resistivity threshold. Ground‐penetrating radar (GPR) and quasi‐3D electrical resistivity tomography (ERT) were combined to indicate permafrost resistivity above 200 Ω‐m. It reveals that the permafrost is relatively stable across a large area on the shaded side, whereas the permafrost degradation is more pronounced on the sunny side, where the maximum active layer thickness (ALT) reaches 5.2 m. Notable permafrost degradation and substantial increases in ALT were observed near the EBTS resulting from heat absorption and thermal erosion of the groundwater. Terrestrial laser scanning (TLS) captured time‐series deformation highlights the specific displacements of the EBTS, demonstrating that the displacement is the rotational behavior of wing walls. The severe heat absorption and groundwater thermal erosion around the EBTS result in permafrost degradation and the expansion of the thawing bulbs to increased structural deformation and even failure. It was shown that permafrost degradation, moisture influence, and heat transfer characteristics are the primary contributing factors to the disease's continued deterioration, and thus reinforcement measures for existing structures need to address these three issues. The mechanisms of disease development revealed in this paper provide new insights into EBTS dynamics for the EBTS design and maintenance in permafrost regions.

In recent years, urban road collapse incidents have been occurring with increasing frequency, particularly in populous cities. To mitigate road collapses, geophysical prospecting plays a crucial role in urban road inspections. Ground Penetrating Radar (GPR), a non-destructive technology, is extensively employed for detecting urban road damage, with manual interpretation of GPR images typically used to identify buried objects. Nonetheless, manual interpretation methods are not only inefficient but also subjective, as they rely on the interpreter's experience, thereby affecting the interpreting reliability. This study investigates the distribution and causes of road collapses and develops a deep learning-based intelligent recognition model using GPR detection images of urban roads in cities of the South China as original samples. The finding reveal that road collapses are concentrated in the months of July and August, mainly caused by pipe leakage and rainfall. Common anomalies in urban road GPR detection comprise seven types of target objects, including cavity, pipeline, etc., with standard GPR images acquired through outdoor field experiments. Utilizing GPR forward simulation and image augmentation methods to expand the sample size, as well as generating anchor box dimensions through clustering analysis, have all been proven to effectively improve the model's performance. The urban road GPR image intelligent recognition model, based on the YOLOv4 algorithm, achieves a detection accuracy of up to 85%, proving effective in GPR detection of urban roads in cities of North China. This research offers valuable insights for the future application of deep learning-based image recognition algorithms in urban road GPR detection.

The Qinghai-Tibet Railway has been operating safely for 16 years in the permafrost zone and the railroad subgrade is generally stable by adopting the cooling roadbed techniques. However, settlement caused by the degradation of subgrade permafrost in the embankment-bridge transition sections (EBTS) is one of the most representative and severe distresses. A field survey on 440 bridges (including 880 EBTSs) was carried out employing terrestrial laser scanning and ground-penetrating radar for comprehensively assessing all EBTSs in the permafrost zone. The results show that the types of distresses of EBTSs were differential settlement, upheaval mounds of the protection-cone slopes, subsidence of the protection-cone slopes, surface cracks of the protection cones and longitudinal and transverse dislocation of the wing walls. The occurrence rates of these distresses were 78.93, 3.47, 11.56, 3.36, 21.18 and 4.56%, respectively. The most serious problem was differential settlement, and the average differential settlement amount (ADSA) was 15.3 cm. Furthermore, the relationships between differential settlement and 11 influencing factors were examined. The results indicate that ADSA is greater on the northern side of a bridge than on the southern side and on the sunny slope than on the shady slope. It is also greater in the high-temperature permafrost region than in the low-temperature permafrost region and in the high-ice content area than in the low-ice content area. The EBTSs are more influenced by ice content than by ground temperature. The ADSA increases when the embankment height increases, the particle size of subgrade soil decreases and the surface vegetation cover decreases.

Highways are the main components of modern transportation hubs, where lots of pavement anomalies have been still occurring with the increasing of the traffic volume and overloading, especially for the coastal developed areas. Rapid nondestructive detection is a necessary means to ensure uninterrupted highway traffic in the road operation period. In this paper, MALA Ground-penetrating Radar is used for detecting the coastal highway pavement anomalies, while the radar signal interpretation is vulnerable to environmental influences and relies too much on experience judgments from engineers. In order to improve the interpretation accuracy, the continuous wavelet transforms are introduced for analyzing multiscale characteristics of the radar signals. Parametric and non-parametric test methods are combined to validate the availability of the processed signals with Reflexw and denoised signals with continuous wavelet transforms. The results show that major pavement anomalies are basically consistent with the radar detection and field survey. These qualitative and quantitative methods for revealing the central location of pavement anomalies, especially for depth, is of importance to assist engineers with reasonable interpretation. Moreover, the accuracy of pavement anomalies determining can be improved from decimeter to millimeter magnitude. Meanwhile, a comprehensive and intuitive interpretation method is provided for engineers combining with one-dimensional and two-dimensional analysis.

Non-destructive detection of pavement diseases on airport runways is of great importance for airport maintenance and ensuring the normal use of airport runways. In this study, a full coverage scan of an airport pavement was conducted using the MALA three-dimensional ground penetrating radar (GPR). Based on complex signal analysis technology, the Hilbert transform were used to comprehensively interpret the anomalous features of the scanned images at specific disease location. The theoretical void volume of pavement diseases was calculated using MATLAB image processing and Origin software. The analysis showed that the main airport pavement diseases were loose layers and small holes, which were more comprehensively revealed by three-dimensional radar images. The three instantaneous parameter spectrums were used to verify and optimize the initial results obtained with the Reflexw post-processing software. The theoretical void volume was calculated using the pixel equivalent method. The void value of typical diseases was 49.38 m³, which was approximately 2.97 times of the calculated value with the rSlicer post-processing technique. This proposed technique can provide a reference for the exact recognition of disease types and the corresponding calculation of grouting reinforcements while overcoming the shortcomings of the single parameter method with multi-parameter analysis.