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The geological and environmental conditions of the northern Shaanxi Loess Plateau are highly fragile, with frequent landslides and collapse disasters triggered by rainfall and human engineering activities. This research addresses the limitations of current landslide hazard assessment models, considers Zhuanyaowan Town in northern Shaanxi Province as a case study, and proposes an integrated model combining the information value model (IVM) with ensemble learning models (RF, XGBoost, and LightGBM) employed to derive the spatial probability of landslide occurrences. Adopting Pearson’s type-III distribution with the Bayesian theorem, we calculated rainfall-induced landslide hazard probabilities across multiple temporal scales and established a comprehensive regional landslide hazard assessment framework. The results indicated that the IVM coupled with the extreme gradient boosting (XGBoost) model achieved the highest prediction performance. The rainfall-induced hazard probabilities for the study area under 5-, 10-, 20-, and 50-year rainfall return periods are 0.31081, 0.34146, 0.4, and 0.53846, respectively. The quantitative calculation of regional landslide hazards revealed the variation trends in hazard values across different areas of the study region under varying rainfall conditions. The high-hazard zones were primarily distributed in a belt-like pattern along the Xichuan River and major transportation routes, progressively expanding outward as the rainfall return periods increased. This study presents a novel and robust methodology for regional landslide hazard assessment, demonstrating significant improvements in both the computational efficiency and predictive accuracy. These findings provide critical insights into regional landslide risk mitigation strategies and contribute substantially to the establishment of sustainable development practices in geologically vulnerable regions.

Geological disasters occur frequently in the Loess Plateau due to the joint fissures in the strata and human engineering activities. Against this background, the deformation and failure mode of the loess slope with the structural plane under excavation and the extension mechanism of the structural plane are analyzed and summarized. The results showed that: (1) Through the physical model test, the deformation failure mode of the slope is summarized as the tension-splitting, pressure-sliding shallow failure. The collapse failure process is defined as four stages: Compression deformation, creep deformation, slip deformation and slip failure. (2) Slope displacement is concentrated beneath the pressure plate, increasing linearly under load conditions but becoming nonlinear after excavation conditions. As the excavation angle rises, the displacement range along the structural plane gradually extends toward the slope toe. The displacement time-history curve shows three stages: The lifting load stage, the cumulating deformation stage, and the sliding failure stage. (3) The stress redistribution caused by excavation, prompting deformation and potential failure. As internal stress nears the soil strength limit, human-induced disturbances exacerbate stress redistribution, leading to accumulated stress. Finally released through deformation and cracking. Each excavation condition modifies the original loading transfer path, driving stress redistribution at the slope surface and at the structural plane’s tip. (4) The sudden drop in stress level and sudden rise of accumulated settlement are the characteristics of slope sliding failure. The position of the structural plane determines the position of the slope sliding surface. (5) According to the external characterization of the structural plane, the extension process of the structural plane can be defined as four stages: Initiation of crack extension, classification deformation, subsection extension and compression sealing. According to the extension of the structural plane, the spreading cracks of the slope’s internal structural plane are defined as two types: Fractured cracks and shear cracks.

The precise evaluation of subsurface soil information is paramount for effective infrastructure design and planning. Geotechnical soil maps (GSMs) play a pivotal role in estimating subsurface properties. As Kabul City, Afghanistan’s largest metropolitan area, undergoes rapid expansion and the demand for safer infrastructure rises, the necessity for precise geotechnical information becomes increasingly urgent. Nevertheless, there is a lack of comprehensive studies on urban geotechnical zoning maps in Afghanistan. Therefore, to this end, this study utilizes inverse distance weighting (IDW) interpolation approaches for the construction and characterization of GSMs using a traditional geographic information system (GIS). Data about soil types and standard penetration (N-value) up to 30 m depth were extracted from 130 locations in New Kabul City, Afghanistan. GSMs incorporating N-value and soil types were constructed utilizing the IDW technique within the ArcGIS platform. The outcomes reveal that the subjected region was dominated by cohesive soil with N-values varying from 7 to 45 in three different sections. The findings exhibit strong empirical correlations between N-values and depth, with an R2 value of 0.95 and an RMSD value of 0.71. Moreover, the correlation coefficient to predict soil type classification is 97%, and it stands at 95% for N-value prediction. These results facilitate the rapid evaluation of subsoil strength and stiffness, offering valuable insights for project planners and feasibility researchers.

The loess structural planes of different formation, scales, origin, and types are widely developed in loess slopes, which can significantly control the structure, hydro-mechanical properties, damage regulations and deformation failure pattern of the slope. A series of major engineering projects have been implemented on the Loess Plateau of China. These projects have formed many loess slopes, which are prone to failure induced by loading. However, the failure mechanism of heap-loading loess slope, especially the influence of structural plane on slope failure, is not clear. Therefore, based on the investigation and analysis of the characteristics of loess structural planes, a large-scale model experiment was carried out, and the deformation process and failure mechanism of loess landslide induced by loading were systematically investigated. The soil pressure distribution, plastic state, and deformation characteristics of the slope were analyzed to reveal the influence of the structural plane on slope failure. The results show that the existence of the structural plane changed the stress field of the loess slope, forming a preferential yielding region around the structural plane, making the structural plane more likely to become a potential sliding surface. Different increases of earth pressure in the x- and z-direction is the main reason for the change in the extension angle of the structural plane. The propagation of the shear zone presents a typical “double slip surface” structure. The failure process of the loess slope induced by loading could be generalized into structural plane extension, shear band initiation, shear band penetration, and sliding failure stages.

The 1920 Haiyuan earthquake in Ningxia induced many loess landslides that still significantly impact the safety of local people. Loess seismic landslides can be mainly divided into three types: the shear-sliding type, seismic-collapsibility type, and liquefaction type, with substantial differences in the deformation characteristics, damage mechanism, and hazard risk. The identification of landslide type can provide a basis for assessing, preventing, and mitigating geological hazards. A total of 1163 landslides (including 141 liquefaction landslides and 775 shear-sliding landslides) were interpreted. The distribution, topography, motion, mechanism, and relevant parameters of the landslides were systematically analyzed. A model of loess seismic landslides classification based on the random forest was proposed. A sample dataset with 21 input parameters of landslide characteristics and one output parameter of landslide type was constructed. Based on the random forest algorithm, the sample dataset was trained with fivefold cross-validation. The parameters in the model were adjusted by a Bayesian optimization algorithm. The model’s generalization ability was verified adopting the receiver operating characteristic (ROC) curves and AUC (the area under the ROC curve) values. The results showed that the classification model has high accuracy and generalization ability (with an accuracy of 92.3% and an AUC value of 0.978). When the landslide classification is carried out for a specific region, the characteristic attributes can be input according to the training samples. The classification model is invoked to set the threshold value of 0.5 and output probability. The landslide type can be determined by the probability subsection according to the output probability P.