This paper presents probabilistic assessment of seismically-induced slope displacements considering uncertainties of seismic ground motions and soil properties. A stochastic ground motion model representing both the temporal and spectral non-stationarity of earthquake shakings and a three-dimensional rotational failure mechanism are integrated to assess Newmark-type slope displacements. A new probabilistic approach that incorporates machine learning in metamodeling technique is proposed, by combining relevance vector machine with polynomial chaos expansions (RVM-PCE). Compared with other PCE methods, the proposed RVM-PCE is shown to be more effective in estimating failure probabilities. The sensitivity and relative influence of each random input parameter to the slope displacements are discussed. Finally, the fragility curves for slope displacements are established for site-specific soil conditions and earthquake hazard levels. The results indicate that the slope displacement is more sensitive to the intensities and strong shaking durations of seismic ground motions than the frequency contents, and a critical Arias intensity that leads to the maximum annual failure probabilities can be identified by the proposed approach.

考虑地震地震动和土壤特性的不确定性，本文提出了地震诱发的边坡位移的概率评估。代表地震震荡的时间和频谱非平稳性的随机地面运动模型和三维旋转破坏机制被集成以评估Newmark型边坡位移。通过将相关向量机与多项式混沌扩展（RVM-PCE）相结合，提出了一种将机器学习纳入元建模技术的新概率方法。与其他PCE方法相比，所建议的RVM-PCE在估计故障概率方面更为有效。讨论了每个随机输入参数对斜率位移的敏感性和相对影响。最后，建立了针对特定土壤条件和地震灾害等级的边坡位移脆弱性曲线。结果表明，边坡位移对地震地震动的强度和强烈的震荡持续时间比频率内容更为敏感，所提出的方法可以识别出导致最大年度失效概率的临界Arias强度。