The objective of this study was to identify the areas that are most susceptible to landslide occurrence, and to find the key factors associated with landslides along Jinsha River and its tributaries close to Derong and Deqin County. Thirteen influencing factors, including (a) lithology, (b) slope angle, (c) slope aspect, (d) TWI, (e) curvature, (f) SPI, (g) STI, (h) topographic relief, (i) rainfall, (j) vegetation, (k) NDVI, (l) distance-to-river, (m) and distance-to-fault, were selected as the landslide conditioning factors in landslide susceptibility mapping. These factors were mainly obtained from the field survey, digital elevation model (DEM), and Landsat 4–5 imagery using ArcGIS software. A total of 40 landslides were identified in the study area from field survey and aerial photos’ interpretation. First, the frequency ratio (FR) method was used to clarify the relationship between the landslide occurrence and the influencing factors. Then, the principal component analysis (PCA) was used to eliminate multiple collinearities between the 13 influencing factors and to reduce the dimension of the influencing factors. Subsequently, the factors that were reselected using the PCA were introduced into the logistic regression analysis to produce the landslide susceptibility map. Finally, the receiver operating characteristic (ROC) curve was used to evaluate the accuracy of the logistic regression analysis model. The landslide susceptibility map was divided into the following five classes: very low, low, moderate, high, and very high. The results showed that the ratios of the areas of the five susceptibility classes were 23.14%, 22.49%, 18.00%, 19.08%, and 17.28%, respectively. And the prediction accuracy of the model was 83.4%. The results were also compared with the FR method (79.9%) and the AHP method (76.9%), which meant that the susceptibility model was reasonable. Finally, the key factors of the landslide occurrence were determined based on the above results. Consequently, this study could serve as an effective guide for further land use planning and for the implementation of development.

Soil in western Jilin, China has the properties of a saline soil, seasonal frozen soil, and a dispersive soil, in which the freezing process can induce many engineering and environmental problems, including soil secondary salinization and frost heave. During the freezing process, water, heat, stress, and even solute interact with each other. Water migration plays an important role in this process. In this paper, a sampling point in Nong’an, western Jilin was selected for soil collection and experimental tests. The experiment included two tests: the capillary water test and a water migration test subjected to freezing. The results showed that the height of capillary water is low, while the water migrates obviously after the process of freezing in different conditions. The results indicate that the bound water film hinders the capillary pores, which can impair the matric potential. During the freezing process, the temperature potential and gravitational potential can drive the water to migrate through the bound water film. For the soil in western Jilin, temperature potential plays a decisive role in water migration, but gravitational potential and matric potential have a minor effects.

In seasonally frozen areas, physical and mechanical soil properties change dynamically under the effect of the freeze-thaw cycles (FTCs), which is a problem that cannot be ignored in geotechnical engineering. In order to study the effect of the FTC on the strength and the mechanism of deformation and failure of saline soil, this paper took Nong’an saline soil as the research object. In total, 105 groups of remolded samples with different salt contents (S) after FTCs were examined in unconsolidated-undrained tests. On the basis of the experiment results, the influence of FTCs on the mechanical properties of Nong’an saline soil was analyzed. The failure principal stress difference (σ₁ − σ₃)_f and cohesion (c) were both decreased with FTC. This occurred especially rapidly after the first cycle and became stable between 30 and 60 cycles. The internal friction angle φ increased at first and then decreased. According to experimental data, a modified Duncan-Chang model was established. Compared with the experiment results, this model was reasonable to simulate the stress-strain relationship of Nong’an saline soil. Furthermore, the empirical formulas of Duncan-Chang model parameters were obtained by regression analysis. This provides a theoretical basis for saline–soil foundation and subgrade engineering in seasonal frozen areas.

Forecasting the occurrence potential of landslides is important but challenging. We aimed to forecast the failure potential of the Helong landslide, which is temporarily stable but has clearly deformed in recent years. To achieve the goal, we used reconnaissance, remote sensing, drilling, laboratory tests, topographical analysis, and electrical resistivity tomography (ERT). The factor of safety (FOS) of the slope was first calculated using a limit equilibrium method. The results show the FOS of the slope was 1.856 under natural conditions, 1.506 under the earthquake conditions, 1.318 under light rainfall, 0.986 under heavy rainfall, 1.075 under light rainfall and earthquake, and 0.832 under simultaneous heavy rainfall and earthquake. When the FOS is less than 1.35, the slope is considered metastable according to the Technical Code for Building Slope Engineering (GB50330-2013) published by the Chinese Ministry of Housing and Urban-Rural Development. Based on the drilling data and digital elevation data, a three-dimensional discrete element method (DEM) model was used to simulate potential landslides. The simulation was used to examine catastrophic slope failure under heavy rainfall conditions within a range of friction coefficients and the corresponding affected areas were determined. Then, we analyzed a typical run-out process. The dynamic information of the run-out behavior, including velocity, run-out distance, and depth, were obtained, which is useful for decision support and future landslide hazard assessment.

With the increase in transportation emissions, road diseases in the saline soil area of Jilin Province have become a problem that requires serious attention. In order to improve the subgrade performance, the structural yield strength (SYS) of remolded soil and its factor sensitivity are investigated in this study. Saline soils in Western Jilin are structural in the sense that the bonding strength of soil skeleton is mainly provided by the solidification bond formed by a physicochemical interaction between particles. Its SYS is influenced by its cementation type, genetic characteristics, original rock structure, and environment. Because of the high clay content in Zhenlai saline soil, the specific surface area of soil particles is large, and the surface adsorption capacity of soil particles is strong. In addition, the main cation is Na⁺. The cementation strength of bound water film between soil particles is thus easily affected by water content and salt content, and compaction is also an important factor affecting the strength of soil. Therefore, in this study, the back-propagation neural network (BPNN) model and a support vector machine (SVM) are used to explore the relationship of saline soil’s SYS with its compactness, water content, and salt content. In total, 120 data points collected by a high-pressure consolidation experiment are applied to building BPNN and SVM model. For eliminate redundant features, Pearson correlation coefficient (r_PCC) is used as an evaluation standard of feature selection. The K-fold cross-validation method was used to avoid over fitting. To compare the performance of the BPNN and SVM models, three statistical parameters were used: the determination coefficient (R²), root mean square error (RMSE), and mean absolute percentage deviation (MAPD). The result shows that the average values of R², RMSE, and MAPD of the BPNN model are superior to the values of the SVM. We conclude that the BPNN model is slightly better than the SVM for predicting the SYS of saline soil. Thus, the BPNN model is used to analyze the factor sensitivity of SYS. The results indicate that the influence degrees of the three parameters are as follows: water content > compactness > salt content. This study can provide a basis for estimating the structural yield pressure of soil from its basic properties, and can provide a new way to obtain parameters for geotechnical engineering, ensuring safety while maintaining symmetry in engineering costs.