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In recent years, landslide disasters in reservoir zones caused by water level fluctuations and continuous rainfall have attracted considerable attention and may threaten the safety of hydropower projects. However, a more comprehensive understanding of the failure mechanism of this type of reservoir soil slope with heterogeneous permeability is needed with increasing similar disasters due to uncertain climate change. On this basis, the current study proposes an intelligent probabilistic framework for evaluating the stability of heterogeneous reservoir soil slopes under such coupled hydraulic conditions. The spatial variability in the soil permeability coefficient is simulated as a uniformly distributed random field and then incorporated into the finite element slope model. The results from Monte Carlo simulation (MCS) indicate that for the slopes subjected to a water head difference but without rainfall, the generated irregular seepage field in random soils changes the mechanical status of the soil skeleton, leading to a variation in the factor of safety (FoS). Under the same reservoir water level (RWL), continuous rainfall lowers the FoS at different scales, while the distribution patterns of FoS histograms maintain highly similar features. Compared with rainfall infiltration, the rise in RWL has a more pronounced influence on destabilizing slope stability because of the greater increase in pore water pressure (POR), which changes the stress state of the soil and reduces its shear strength correspondingly. In addition, although the multi-scale horizontal correlation lengths of the soil permeability coefficient random field exert a pronounced influence on the irregular seepage field within slopes, its impact on the overall slope stability is limited. These findings can serve as a technical reference for optimizing the design of intelligent monitoring system for reservoir soil slopes, which have important practical implications for advancing the dynamic risk management of reservoir landslide hazards and improving the corresponding disaster prevention and mitigation strategies in a more reliable way.
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