The steep bedding rock slope (SBRS) is easily destabilized under earthquake action, so it is crucial to research the features of this kind of slope’s seismic dynamic reactions in order to prevent and mitigate disasters. Few researchers have examined these slopes from an energy perspective, and the majority of recent research focuses on the displacement and acceleration response patterns of these kinds of slopes under seismic action. This work performed an extended study of a dynamic numerical simulation and systematically analyzed the dynamic response characteristics of this type of slope under earth quake conditions from the standpoint of energy utilizing the Hilbert–Huang transform (HHT) and marginal spectrum (MSP) theory. This was carried out in response to the slope’s shaking table test from our previous work. The findings indicate the following: (1) The ‘elevation effect’ and ‘surface effect’ are clearly seen in the acceleration amplification factor (AAF) of the slope during an earthquake. The selectivity of the slope acceleration’s Fourier spectrum amplification impact indicates that the elevation amplification effect makes the high-frequency peak’s amplitude more noticeable. (2) Although the effect of the weak layer is more pronounced in the high-frequency portion, both the elevation and the weak layer affect the seismic wave’s Hilbert energy. As a result, the weak layer at the top of the slope is usually destroyed first during an earthquake. (3) Prior to the locked segment’s penetration failure at the toe of the SBRS, the Hilbert energy of the high-frequency band of the marginal spectrum at the monitoring point on the top portion of the segment will rise sharply. This suggests that the upper portion of the locked segment has begun to sustain damage. There are antecedents even when there is no penetration failure.

The long-term stability of slopes in areas with strong earthquakes not only is very important for people’s lives and the safety of property, but also it enables restoration of the ecological environment in the landslide areas, which is very important for sustainable development. The most commonly used seismic-support method, anti-slide piles, provides outstanding seismic performance. However, piles still deform and fail during earthquakes, which can lead to instability of the slope. The dynamic response of a slope reinforced with anti-slide piles is crucial for maintaining the long-term stability of the slope in a strong-earthquake area and, thus, for promoting its sustainable development. However, current research is focused mainly on the stability of the slope, and there have been few studies on the dynamic response of anti-slide piles. For this reason, we have undertaken the present study of a bedding-rock slope supported by a single row of anti-slide piles. By changing the frequency, amplitude, and duration of the input seismic waves, we have systematically explored the influence of their spectral characteristics on the dynamic response of the anti-slide piles and the slope using numerical simulations combined with the wavelet-transform method. Our results show that the spectral characteristics of the seismic waves significantly affect the deformations of the anti-slide piles. Low-frequency and high-amplitude seismic waves have stronger destructive effects on slopes, and high-amplitude seismic waves can generate multi-level sliding surfaces that extend to deeper levels. The low-frequency component of the seismic wave controls the overall deformation of the slope, and the high-frequency component controls the local deformations. An increase in the proportion and duration of low frequencies in seismic waves is the main cause of slope deformation and failure. The present work, thus, provides a useful reference for the design of a slope supported by anti-slide piles in an area with strong earthquakes, as well as for the maintenance of the long-term stability of such a slope, therefore, encouraging the sustainable development of related areas.

In this study, landslide risk assessment was conducted in the Renhe District, Panzhihua City, China. Firstly, based on 190 landslide points and 10 influencing factors, the landslide hazard was assessed using three models: random forest (RF), eXtreme Gradient Boosting (XGBoost), and Tabular Prior-data Fitted Network (TabPFN). The results indicate that the RF and XGBoost models exhibit comparable performance, both demonstrating strong generalization and accuracy, with the RF model achieving superior generalization, as evidenced by an area-under-the-curve (AUC) value of 0.9471. While the AUC value of TabPFN is 0.9243, indicating higher accuracy, it also poses a risk of overfitting and is therefore more suitable for applications involving small sample sizes and the need for rapid responses. The vulnerability assessment utilized the Analytic Hierarchy Process (AHP) to determine the weights of four disaster-bearing bodies, with sensitivity analysis revealing that road type was the most sensitive vulnerability factor. Finally, the landslide risk-assessment map of the Renhe District was produced by integrating the landslide hazard assessment map with the vulnerability assessment map. The findings indicate that the high-risk zones comprised 2.08% of the research region, which includes three principal train stations and necessitates enhanced protective measures. The medium-risk zones comprise 34.23% of the total area and are scattered throughout the region. It is important to enhance local capabilities for landslide monitoring and early warning systems. Relevant conclusions can provide a significant reference for landslide disaster prevention and mitigation work in the Renhe District and help ensure the safe operation of public transport infrastructure, such as railway stations and airports in the district.

To reveal the influence of dynamic load on the dynamic damage behavior of jointed rock masses, split Hopkinson pressure bar (SHPB) system was employed to test platy gabbro samples with joint angles of 15°, 30°, 45°, 65°, and 75° at an impact pressure of 0.1 MPa. It was found that the joint angle has a significant effect on the dynamic characteristics and fracture behaviors of the echelon gabbro samples. Samples with a joint angle of 15° required the least amount of fracture energy and was most prone to failure, while samples with a joint angle of 30° required the most amount of fracture energy and was difficult to fracture. Moreover, the joint angle controlled the damage mode of the specimen. Specimens with 15° and 30° joint angles corresponded to crack coalescence type I, where the main crack was a shear crack and was nearly straight. Specimens with 45° and 60° joint angles corresponded to crack coalescence type II, where the main crack was a shear crack, and a small number of tensile secondary cracks developed. Specimens with a joint angle of 75° corresponded to crack coalescence type III, where the main crack was a shear crack and a large number of tensile cracks developed.

Landslides are frequent and destructive geological disasters. Accurate landslide identification is essential for post-disaster reconstruction and preventing secondary disasters. Deep learning has shown considerable potential for recognizing landslide objects from remote-sensing images; however, existing models still suffer from insufficient detection accuracy in scenarios with complex backgrounds, blurred boundary localization, sample-class imbalance, and difficulty in balancing detection speed and segmentation accuracy. To address these issues, this study investigates landslide identification in the Great Bend of the Yarlung Zangbo River region using improved deep learning models and heterogeneous optical remote-sensing imagery. (1) By introducing the convolutional block attention module (CBAM) into YOLOv8, 82.79% precision was achieved, and the recall improved by 9.56% compared to the original model, reaching a mean average precision of 75.27% while maintaining computational efficiency, outperforming YOLOv5 and the original YOLOv8. (2) Replacing the cross-entropy loss with Focal Loss in DeepLabV3+ improved the landslide edge segmentation by dynamically adjusting the weights of difficult and easy samples. Compared with the original DeepLabV3+, the precision and recall of the DeepLabV3+-FL semantic-segmentation model were improved by 0.26% and 1.93%, respectively, with the mean pixel accuracy and mean intersection over union reaching 81.76% and 62.59%, respectively. Overall, the two improved models enhanced the accuracy of landslide identification and resistance to interference, demonstrating potential for landslide monitoring and emergency response.