The present paper focuses on designing an artificial neural network (ANN) model and a multiple regression analysis (MRA) that could be used to predict factor of safety of dragline dump slope. To implement these two models, the dataset was utilized from the numerical simulation results of dragline dump slopes, wherein 216 dragline dump slope models were simulated using a numerical modeling technique employed with the finite element method. The finite element model was incorporated a combination of three geometrical parameters, namely, coal-rib height (Crh), dragline dump slope height (Sh), and dragline dump slope angle (Sa) of the dump slope. The predicted results derived from the MRA and ANN models were compared with the results obtained from the numerical simulation of the dump slope models. Moreover, to compare the validity of both the models, various performance indicators, such as variance account for (VAF), determination coefficient (R²), root mean square error (RMSE), and residual error were calculated. Based on these performance indicators, the ANN model has shown a higher prediction accuracy than the MRA model. The study reveals that the ANN model developed in this research could be handy in designing the dragline dump slopes at the preliminary stage.

本文着重设计人工神经网络（ANN）模型和多元回归分析（MRA），这些模型可用于预测拉铲铲斗边坡的安全系数。为了实现这两个模型，从铲斗倾卸坡的数值模拟结果中利用数据集，其中使用有限元方法采用的数值建模技术模拟了216铲斗倾卸坡的模型。结合了三个几何参数的有限元模型，即煤肋高度（Crh），铲斗倾卸倾角高度（Sh）和铲斗倾卸倾角（Sa））的倾卸坡度。将MRA和ANN模型得出的预测结果与倾卸坡模型的数值模拟得到的结果进行比较。此外，为了比较两个模型的有效性，计算了各种性能指标，例如方差占比（VAF），确定系数（R ²），均方根误差（RMSE）和残留误差。基于这些性能指标，ANN模型显示出比MRA模型更高的预测准确性。研究表明，本研究开发的人工神经网络模型可以在初期阶段设计拉铲铲斗倾卸边坡。