The phenomenon of landslide spatial aggregation is widespread in nature, which can affect the result of landslide susceptibility prediction (LSP). In order to eliminate the uncertainty caused by landslide spatial aggregation in an LSP study, researchers have put forward some techniques to quantify the degree of landslide spatial aggregation, including the class landslide aggregation index (LAI), which is widely used. However, due to the limitations of the existing LAI method, it is still uncertain when applied to the LSP study of the area with complex engineering geological conditions. Considering landslide spatial aggregation, a new method, the dual-frequency ratio (DFR), was proposed to establish the association between the occurrence of landslides and twelve predisposing factors (i.e., slope, aspect, elevation, relief amplitude, engineering geological rock group, fault density, river density, average annual rainfall, NDVI, distance to road, quarry density and hydropower station density). And in the DFR method, an improved LAI was used to quantify the degree of landslide spatial aggregation in the form of a frequency ratio. Taking the middle reaches of the Tarim River Basin as the study area, the application of the DFR method in an LSP study was verified. Meanwhile, four models were adopted to calculate the landslide susceptibility indexes (LSIs) in this study, including frequency ratio (FR), the analytic hierarchy process (AHP), logistic regression (LR) and random forest (RF). Finally, the receiver operating characteristic curves (ROCs) and distribution patterns of LSIs were used to assess each LSP model’s prediction performance. The results showed that the DFR method could reduce the adverse effect of landslide spatial aggregation on the LSP study and better enhance the LSP model’s prediction performance. Additionally, models of LR and RF had a superior prediction performance, among which the DFR-RF model had the highest prediction accuracy value, and a quite reliable result of LSIs.

Landslide risk assessment (LRA) is an important basis for disaster risk management. The widespread phenomenon of landslide spatial aggregation brings uncertainty to landslide hazard assessment (LHA) in LRA studies, but it is often overlooked. Based on the frequency ratio (FR) method, we proposed the dual-frequency ratio (DFR) method, which can quantitatively analyze the degree of landslide spatial aggregation. Using the analytic hierarchy process (AHP) and random forest (RF) models, we applied the DFR method to the LRA study of the Karakoram Highway section in China. According to the receiver operating characteristic (ROC) curve and the distribution characteristics of landslide hazard indices (LHIs), we evaluated the application effect of the DFR method. The results showed that the LHA models using the DFR method performed with higher accuracy and predicted more landslides in the zones with a high LHI. Moreover, the DFR-RF model had the best prediction performance, and its predictions were adopted together with vulnerability values to calculate the landslide risk. The zones with very high and high landslide risks were predominantly concentrated along highways in southern Aoyitake Town.

Surface roughness is ubiquitous in hard rock discontinuities at different scales and plays a critical role in governing frictional behavior and rupture dynamics. In this study, triaxial shear tests were conducted on sawcut smooth fractures and tension-induced rough fractures to investigate frictional behavior, roughness evolution, and rupture dynamics with increasing shear cycles. The results demonstrate that rough fractures exhibit higher shear strength and more intense stick-slip behavior than smooth fractures, but show strength weakening and reduced stress drops with shear cycle. In contrast, smooth fractures display relatively stable strength and stress drops. These differences in frictional behavior are governed by roughness evolution. Although roughness decreases in both fracture types after shearing, rough fractures experience degradation nearly an order of magnitude greater than that of smooth fractures. The initial stick-slip event on rough fractures generates the largest stress drop and apparent breakdown work. In addition, analyses of stress drop and energy dissipation reveal that friction drops for different types of fracture are concentrated within the range of 0.01 to 0.3. These findings highlight the critical role of roughness evolution in fault stability and provide valuable insights for seismic hazard assessment in deep underground engineering.

Under the complex geological structural stress of the Western Himalayan syntaxis, the widespread distribution of hard and brittle rocks (such as sandstone and limestone) makes them prone to the formation of conjugate joints, also known as X-joints. These joints create weak structural planes in the slope rock mass, and when combined with weak interlayers within the slope, they result in a complex dynamic response and hazard situation in this region, which is further exacerbated by frequent seismic activity. This poses a serious threat to the planning, construction, and safe operation of the Belt and Road Initiative. To study the slope vibration response and instability mechanisms under these conditions, we conducted a shaking table test using the Iymek avalanche as a case study and performed Hilbert–Huang Transform (HHT) analysis. We also compared the results of the shaking table test on slope models without X-joints but containing weak interlayers. The findings show that the presence of X-joints leads to an earlier onset of plastic failure in the slope. During the failure development, X-joints cause stress concentration and the diversification of stress redistribution paths, delaying energy release. Ultimately, the avalanche failure mode in the X-joint slopes is more dispersed compared to the landslide failure mode in the model without X-joints. At the toe of the slope beneath the weak interlayer, low-frequency seismic waves can cause a significant amplification of acceleration, and the weak interlayer is often the shear outlets of the slope. These findings provide new insights into the seismic failure evolution of jointed slopes with weak interlayers and offer practical references for seismic hazard mitigation in mountainous infrastructure.

This study examines the influence of tectonically induced mineralogical and microfabric changes on the strength of different rocks within the Hanzel Fault Damage Zone (FDZ) in the Tethyan Himalayas, Pakistan. Integrating field observations, petrographic analysis, and laboratory experiments (uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), ultrasonic pulse-wave velocity (UPV), and porosity), this study systematically characterizes the spatial variations in intact rock strength across horizontal distance from the fault core to the outer limit of the FDZ. Seven rock units—granites (biotite granite, leucogranite schist, granodiorite schist, and diorite) and amphibolites (foliated amphibolite, amphibolite, and plagioclase amphibolite)—were sampled at varying distances (−500 to +4035 m) from the fault core. Results reveal that proximity to the fault core correlates with significant strength reductions (40%–70%): granitic rocks exhibit lower UCS (41–59 MPa) and BTS (4.8–6.7 MPa) compared to distal amphibolites and diorites UCS (75–107 MPa) and BTS (10–13.67 MPa). Petrographic analysis identifies key factors that reduce strength, including high mica content (up to 33%), pervasive micro-fracturing, S-C fabrics, and mineral alteration. These features increase porosity (up to 1.21%) and reduce UPV (2867–3315 m/s) in fault-proximal rocks. Moderate inverse relationships (R² = 0.68–0.72) between mica percentage and UCS/UPV confirm phyllosilicates as primary strength controls. The spatial variation in rock strength is attributed to ductile–brittle deformation processes, with foliated or schistose textures increasing in proximity to the fault core. This study demonstrates that tectonic processes significantly influence the mineralogy and microfabric within FDZs, leading to variations in rock strength with direct implications for stability in tectonically active regions.

The phenomenon of landslide spatial aggregation is widespread in nature, which can affect the result of landslide susceptibility prediction (LSP). In order to eliminate the uncertainty caused by landslide spatial aggregation in LSP study, scholars have proposed some methods to quantify the degree of landslide spatial aggregation, such as class landslide aggregation index (LAI), which is widely used. However, due to the limitations of the existing LAI method, it is still uncertain when applied to the LSP study of the area with complex engineering geological conditions. Considering the landslide spatial aggregation, a new method, dual frequency ratio (DFR), was proposed in this paper to establish the relationship between the landslide occurrence and twelve predisposing factors (namely, slope, aspect, elevation, relief amplitude, engineering geological rock group, fault density, river density, annual average rainfall, NDVI, distance to road, quarry density and hy-dropower station density). And in the DFR method, an improved LAI was used to quantify the degree of landslide spatial aggregation in the form of frequency ratio. Taking the middle reaches of Tarim River Basin as the study area, the application of DFR method in LSP study was verified. Meanwhile, four models were adopted to calculate the landslide susceptibility indexes (LSIs) in this study, including frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR) and random forest (RF). Finally, the prediction performance of each LSP model was evalu-ated by the receiver operating characteristic curves (ROCs) and distribution patterns of LSIs. The results showed that DFR method could reduce the adverse effect of landslide spatial aggregation on LSP study and enhance the prediction performance of LSP model better. In addition, models of LR and RF had superior prediction performance, among which DFR-RF model had the highest prediction accuracy value and quite reliable result of LSIs.