To study the stability evaluation method and failure mode of reserved rock masses, the Yueyang landslide project was taken as the research object. First, based on the limit equilibrium method and elastic mechanics, the formulas for calculating the stability of a reserved rock mass were analyzed. Second, the failure modes of the reserved rock mass were tested by the experiment model. The results show that the failure modes of the reserved rock mass can be divided into three modes: upward sliding failure, downward sliding failure and tensile crack failure, which are mainly related to the strength and width of the reserved rock mass. Therefore, it is unreasonable to regard a reserved rock mass as a unified failure form in the design of anti-slide pile reinforcement. In addition, although both moderate-strength and strong-strength reserved rock masses exhibit tensile crack failure, moderate-strength rock masses under triangular loading are prone to tension-sliding failure, while strong-strength rock masses under parabolic loading are prone to tension-overturning failure. Finally, the displacement and stress monitoring results in the experiment are basically consistent with the theoretical analysis, indicating that the theoretical analysis results have high reliability. Landslide treatmentAnti-slide pile reinforcement Reserved rock Sliding failure Factor of safety

The stability of high backfill slopes emerges in practice due to the expansion of transportation infrastructures. The seepage and infiltration of rainfall into the backfills brings challenges to engineers in predicting the stability of the slope, weakening the shear strength and modulus of the soil. This study carried out a series of model tests under a plane strain condition to investigate the stability of a high backfill slope moisturized by rainfalls, considering the influences of rainfall duration and intensity. The slope displacements were monitored by a laser displacement sensor and the moisture content in the backfill mass were obtained by a soil moisture sensor. The test results show that increasing the rainfall intensity and duration caused the slope near the surface to be saturated, resulting in significant influences on the lateral displacement of the slope and the reduction of stability as well as the sizes of the sliding mass. Based on the model tests, the numerical analysis was adopted to extend the analysis cases, and the backpropagation (BP) neural network model was further adopted to build a model for predicting the stability of a high backfill slope under rainfall. The trained BP model shows the average relative error of 1.02% and the goodness of fitness of 0.999, indicating a good prediction effect.

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