Fuzzy Comprehensive Evaluation Method for Evaluating Stability of Loess Slopes

The stability assessment of loess slopes is of great significance for slope reinforcement and safety assessment. This research studies the main factors affecting the stability of the loess slope through the summary and analyzes the failure cases of the loess slope in Shaanxi Province. The importance of influencing factors was studied through numerical simulation method, sensitivity analysis method, and gray correlation analysis method, and the weight value method was given. On this basis, we have developed the fuzzy comprehensive evaluation model method for assessing the stability of loess slopes based on the principle of maximum membership degree. Finally, the method was applied to the stability analysis of the actual loess slope, and the rationality and correctness of the loess slope stability evaluation method proposed in this paper were demonstrated. The results showed that, for the Shaanxi loess slope, the probability of instability of the positive slopes is far greater than that of negative slopes; the greater the slope gradient, the more unstable the loess slopes. Collapse mainly occurs in the range of 10–40 m slope height. There is a significant positive correlation between rainfall and the probability of loess landslides. The degree of correlation between the factors influencing slope stability and the safety factor can be categorized from strong to weak as follows: slope inclination > internal friction angle > height of the slope > gravitational forces > cohesion > Poisson’s ratio > modulus of elasticity, and the influence of Poisson’s ratio and elastic modulus can be ignored. The fuzzy comprehensive evaluation method based on the gray correlation degree method established in this paper was used to evaluate the stability of the loess slopes. The evaluation results attested to the actual data of slope monitoring. The evaluation method proves reasonable and feasible and can be well applied to the stability analysis of the loess slopes.