The study proposes a framework combining machine learning (ML) models into a logical hierarchical system which evaluates the stability of the sheet wall before other predictions. The study uses the hardening soil (HS) model to develop a 200-sample finite element analysis (FEA) database, to develop the ML models. Consequently, a system containing three trained ML models is proposed to first predict the stability status (random forest classification, RFC) followed by 1) the cantilever top horizontal displacement of sheet wall (artificial neural network regression models, RANN1) and 2) vertical settlement of soil (RANN2). The uncertainty of this data-driven system is partially investigated by developing 1000 RFC models, based on the application of random sampling technique in the data splitting process. Investigation on the distribution of the evaluation metrics reveals negative skewed data toward the 1.0000 value. This implies a high performance of RFC on the database with medians of accuracy, precision, and recall, on test set are 1.0000, 1.0000, and 0.92857, respectively. The regression ANN models have coefficient of determinations on test set, as high as 0.9521 for RANN1, and 0.9988 for RANN2, respectively. The parametric study for these regressions is also provided to evaluate the relative insight influence of inputs to output.

该研究提出了一个框架，将机器学习 (ML) 模型结合到一个逻辑层次系统中，该系统在其他预测之前评估板墙的稳定性。该研究使用硬化土壤 (HS) 模型开发 200 个样本的有限元分析 (FEA) 数据库，以开发 ML 模型。因此，提出了一个包含三个经过训练的 ML 模型的系统，首先预测稳定性状态（随机森林分类，RFC），然后是 1）板墙的悬臂顶部水平位移（人工神经网络回归模型，RANN1）和 2）垂直沉降土壤（RANN2）。基于随机抽样技术在数据拆分过程中的应用，通过开发 1000 个 RFC 模型部分研究了该数据驱动系统的不确定性。对评估指标分布的调查显示，数据向 1.0000 值倾斜。这意味着 RFC 在数据库上的高性能，准确率、精度和召回率的中位数在测试集上分别为 1.0000、1.0000 和 0.92857。回归 ANN 模型在测试集上的确定系数分别高达 RANN1 和 RANN2 的 0.9521 和 0.9988。还提供了这些回归的参数研究来评估输入对输出的相对洞察力影响。RANN1 为 9521，RANN2 为 0.9988。还提供了这些回归的参数研究来评估输入对输出的相对洞察力影响。RANN1 为 9521，RANN2 为 0.9988。还提供了这些回归的参数研究来评估输入对输出的相对洞察力影响。