Traditional landslide hazard assessment methods generally use the same evaluation model to carry out assessments under different working conditions. Due to the differences in landslide influence factors and model calculation methods considered under different working conditions, the evaluation results obtained using traditional methods are different from those of real-world scenarios. Therefore, research on optimal landslide hazard assessment methods for different working conditions is important for disaster prevention and mitigation in areas along fault zones. Taking the section along the Lixian–Luojiabu fault zone in Gansu province in China as the research area, a landslide hazard assessment was carried out under rainfall and earthquake conditions. A method based on the fractal theory–information coupling model is proposed for the rainfall condition, and a method based on an improved Newmark model considering matric suction is proposed for the earthquake condition. Under the rainfall condition, a landslide hazard assessment was carried out using the information model, the logistic regression model, the fractal theory model, the logistic regression–information coupling model and the fractal theory–information coupling model. Meanwhile, under the earthquake condition, an assessment was carried out using the traditional Newmark model and the improved Newmark model considering matric suction. Finally, the ROC curve and Kappa coefficient were used to test the accuracy of these evaluation models and to determine the optimal model under different working conditions. The results showed that the fractal theory–information coupling model had the largest AUC value and Kappa coefficient value under the rainfall condition (0.856 and 0.807, respectively). The test value of the logistic regression–information coupling model was second, and the values of the other three models were all lower than 0.8. This shows that the evaluation of the fractal theory–information coupling model is better than those of the other models under the rainfall condition. The AUC value and Kappa coefficient of the improved Newmark model under the earthquake condition were 0.805 and 0.794, respectively, which were larger than the test values of the traditional Newmark model. This shows that the evaluation of the proposed model is better than that of the traditional Newmark model under the earthquake condition. These research results provide a reference for landslide hazard assessments in areas with similar characteristics.

The structural index is an important quantitative parameter for revealing the structural properties of loess. However, there is no a widely accepted measurement method for structural index at present. This study aims at presenting a novel method for obtaining the loess structural index (LSI), based on the application of computed tomography (CT) scanning techniques and laboratory physico-mechanical tests. The mountainous area of Lvliang in northwest China was taken as the study area, and Late Pleistocene loess samples were taken from various sites in the region. Several physical parameters were first measured using laboratory tests, including dry density, pore ratio, and liquidity index. CT scanning was used to observe sample microstructures, and a mathematical relationship was established between CT image parameters and the physical property indices, through three dimensions (3D) reconstruction and slice porosity analysis. The results revealed that LSI can be expressed as a non-linear function related to CT image parameters, dry density, and the liquidity index of the loess. Compared with traditional calculation methods, this novel technique calculates the LSI by using an empirical formula, which is less labor-intensive. Such results indicate that the method warrants wide application in the future.