Peifeng He’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.

Changes of the lakes on high-altitude regions of the Tibet Plateau (TP) influence the state of the surrounding permafrost. Due to the climate warming and wetting trend, extreme events including lake outburst has occurred more frequent. In 2011, an outburst event occurred on the Zonag Lake and this event changed the water distribution in the basin, leading a rapid expansion of the tailwater lake, named as the Salt Lake. However, the construction of the drainage channel in the Salt Lake ended the expansion process and the shrinkage of the lake started since 2020. To investigate the permafrost state around the Salt Lake, multiple methods, including drilling boreholes, the unmanned aerial vehicle (UAV) survey and the GPR detection have been applied. By integrating these multi-source data, the thermal regime, topography and the spatial distribution of the permafrost around the Salt Lake were analyzed. The result showed that the permafrost state around the Salt Lake was related to the distance from the lake water. The permafrost table appears at 90 m away from the Salt Lake and interrupted by a nearby thermokarst lake at 220 m. The ground temperature in the natural field is 0.2 ℃ lower than the temperature in the lake at a depth of – 5 m.

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.

Retrogressive thaw slumps (RTSs), which frequently occur in permafrost regions of the Qinghai-Tibet Plateau (QTP), China, can cause significant damage to the local surface, resulting in material losses and posing a threat to infrastructure and ecosystems in the region. However, quantitative assessment of ground ice ablation and hydrological ecosystem response was limited due to a lack of understanding of the complex hydro-thermal process during RTS development. In this study, we developed a three-dimensional hydro-thermal coupled numerical model of a RTS in the permafrost terrain at the Beilu River Basin of the QTP, including ice–water phase transitions, heat exchange, mass transport, and the parameterized exchange of heat between the active layer and air. Based on the calibrated hydro-thermal model and combined with the electrical resistivity tomography survey and sample analysis results, a method for estimating the melting of ground ice was proposed. Simulation results indicate that the model effectively reflects the factual hydro-thermal regime of the RTS and can evaluate the ground ice ablation and total suspended sediment variation, represented by turbidity. Between 2011 and 2021, the maximum simulated ground ice ablation was in 2016 within the slump region, amounting to a total of 492 m ³ , and it induced the reciprocal evolution, especially in the headwall of the RTS. High ponding depression water turbidity values of 28 and 49 occurred in the thawing season in 2021. The simulated ground ice ablation and turbidity events were highly correlated with climatic warming and wetting. The results offer a valuable approach to assessing the effects of RTS on infrastructure and the environment, especially in the context of a changing climate.

Thermokarst terrain developed acceleratingly in ice‐rich permafrost on the Qinghai‐Tibet Plateau (QTP), China, and the most dramatic among these terrain‐altering thermokarsts are retrogressive thaw slump (RTS). Although the freeze‐thaw erosion (FTE) impacts are sharply increasing on the plateau due to RTS, especially for migration of fine sediments in cold climate, these impacts are still not quantified due to the limitation of hydro‐thermal‐mass transport laws of RTS development. Moreover, it is difficult to assess the impact of RTS on the eco‐environment, especially soil erosion. This study developed a heat‐water‐mass transport coupled model of a RTS in the Beiluhe River Region on the QTP, considering the actual topography, water‐ice phase change and latent heat, and surface heat exchange layer. Based on the observed data of ground temperature, unfrozen water content, and heat flux, the coupled model herein is practicable for presenting the geotemperature regime and groundwater flow in RTS area, thereby quantifying the ice‐rich permafrost thaw and mass wasting. The results presented that: 1) the seepage velocity of the superficial zone (0 to 1.5 m depth) is two orders of magnitude higher than that of the permafrost table; 2) the mean ice‐rich permafrost thaw volume was 13.4 m2 from 2016 to 2021; 3) the cumulative mass transport volume was 22 m2 from July 2020 to September 2021. Additionally, the relation between the FTE (showing as migration of sediments) and the amount of ground ice ablation can be fitted by an exponential equation. This work proposed a reliable method for quantifying the effect of FTE and is helpful to assess the eco‐environment impacts of RTS.