Engineers often use semiempirical models, which estimate the amount of seismically-induced slope displacements (D), to evaluate the seismic performance of earth structures and natural slopes. These procedures often use as inputs slope properties, earthquake parameters, and ground motion intensity measures (IMs). In this study, we propose a new set of machine learning (ML) based models to estimate D using the NGA-West2 shallow crustal ground motion database. We consider both the classification of negligible D and its estimation. The selection of features to explain D (which is based on LASSO, Forward Selection, and Random Forest) suggests that the most efficient features are the slope's yield coefficient (k_y), its fundamental period (T_s), the earthquake magnitude (M_w), the peak ground velocity (PGV), and the degraded spectral acceleration at 1.3 T_s. Moreover, the feature selection suggests that there is no significant gain in accuracy beyond five features. We formulate 19 different models, considering various ML-based algorithms such as Generalized Linear Models (GLM), Partial Least Square Regressions (PLSR), Principal Component Regressions (PCR), Bagging and Boosting, Random Forest, Polynomial-based regressions, Multi-order regressions, and Kernel-based models. We assess the performance of the proposed models by evaluating test errors, their predictive performance in case histories, and comparisons against existing models. Based on the assessments, we recommend 6 ML-based models to estimate D in engineering practice.

工程师经常使用半经验模型来估计地震引起的边坡位移量 ( D )，以评估土体结构和天然边坡的抗震性能。这些程序通常使用坡度特性、地震参数和地震动强度测量 ( IM )作为输入。在这项研究中，我们提出了一组新的基于机器学习 (ML) 的模型，以使用 NGA-West2 浅层地壳地面运动数据库来估计D。我们同时考虑可忽略D的分类及其估计。解释D的特征选择（基于 LASSO、前向选择和随机森林）表明最有效的特征是斜率的屈服系数（ķ _Ý），其基本周期（Ť_小号），地震幅度（中号_瓦特），峰值地面速度（PGV），并在1.3降解谱加速度 Ť_小号. 此外，特征选择表明超过五个特征的准确性没有显着提高。我们制定了 19 种不同的模型，考虑了各种基于 ML 的算法，例如广义线性模型 (GLM)、偏最小二乘回归 (PLSR)、主成分回归 (PCR)、Bagging 和 Boosting、随机森林、基于多项式的回归、多顺序回归和基于内核的模型。我们通过评估测试错误、案例历史中的预测性能以及与现有模型的比较来评估所提出模型的性能。基于评估，我们推荐了 6 个基于 ML 的模型来估计工程实践中的D。