This study aims to explore the differences between the loess and landslide deposits, focusing on aspects such as particle distribution, consolidation characteristics, and the dynamic shear modulus. Through a series of experiments, the research reveals the similarities and differences between these two entities, yielding several key findings. Firstly, the process of landsliding disrupts the original structure of the loess, resulting in a reduction in porosity and a densification of the soil. Additionally, the movement and sorting of particles during the landslides cause variations in particle size distribution across different sections of the landslide deposits. Secondly, the landslide process not only alters the soil’s structure but also changes the particle sizes within the loess. Particle wear and sieving results in the transformation of larger particles into smaller ones, leading to a more uniform particle size distribution. This shift in the structure and particle size directly impacts the consolidation characteristics of landslide deposits, resulting in a substantial reduction in the compression coefficient. Despite undergoing consolidation for decades, the middle and lower sections of the landslide deposits still exhibit under-consolidation. Although the differences in the maximum dynamic shear modulus between the loess and landslide deposits at varying depths are relatively minor, differences in the porosity and consolidation characteristics lead to faster decay rates of the dynamic shear modulus for the latter. The study also highlights a reduction in the water sensitivity of the maximum dynamic shear modulus within the landslide deposits. Based on the experimental results, a predictive model is proposed, utilizing the A and m values to estimate the maximum dynamic shear modulus of both the loess and landslide deposits. In conclusion, this research uncovers the impact of landslide processes on the structure and properties of the loess, providing an insightful understanding into the disparities between these two entities.

Bedrock fault dislocation is a crucial structural factor influencing landslide movement. Accurately predicting the location and scale of rupture zones within a slope body is essential for effective slope construction design and risk mitigation. Based on an analysis of seismic damage in slope cross-bedrock faults, this article creatively realizes the physical model test of the slope and its covering layer site with soil rupture zones at the top and toe of the slope caused by the dislocation of the bedrock normal fault. Through the model test, macroscopic phenomena were observed, and microscopic analysis was obtained by deploying sensors. The main results were as follows: (i) The evolutionary process of the instability mechanism could be divided into three stages: crack damage stage (Stage I), crack expansion and penetration stage (Stage II), and slope instability stage (Stage III). (ii) Two rupture modes of the soil body in the slope under bedrock dislocation were identified, with the rupture mode at the slope crest having a greater impact on the soil slope. (iii) Inferring the position of bedrock faults through the location of the main rupture zones on the slope surface represents a feasible supplementary method for identifying seismogenic structures during field surveys. These research results provide a scientific basis for the stability assessment of cross-fault slopes and the reinforcement design of landslide disasters.

Utilizing the database of seismic landslides and strong motion records from the Loess Plateau, 81 earthquakes with magnitudes ranging from 3.0 to 7.1 were undertaken. Furthermore, 2802 strong ground motion records were chosen as the focal points of our investigation. Through analysis of the variation pattern of effective peak acceleration (EPA) values, an EPA prediction model for the Loess Plateau was developed using both surface wave magnitude (M_s) and Das magnitude (M_wg). Furthermore, the study examines the discrepancies between this model and two other attenuation models. The results show that (1) the \(\overline{\beta }\)_max (maximum average amplification factor) values employed for the Loess Plateau differ from those specified in other established standards and span from 2.15 to 2.40 in the horizontal and vertical directions. (2) A negative correlation between EPA values and epicentral distance. The magnitudes and directions influence the rates of change of the fitting curves. (3) A positive correlation between the EPA values and earthquake magnitudes. The change rates of the fitting curves tend to increase first and then decrease, with the horizontal directions always being higher than the vertical directions. (4) The prediction equation based on M_wg exhibits residual standard deviations in the range of 0.493–0.536, which optimizes the fitting results and simplifies the fitting procedure to some extent. (5) The prediction model for the Loess Plateau differs from the reference models, with its fitting EPA values and attenuation rates being relatively lower.