关键词: 遗传算法(GA), 反向传播(BP)神经网络, 露天矿山, 排土场, 边坡失稳预测, 安全系数

Abstract:

To improve the prediction accuracy and reliability of slope instability in mine waste dumps, a hybrid GA-BP model was developed by integrating an improved GA with a BP neural network. The model employed GA to globally optimize the initial weights and thresholds of the BP network, and incorporated the Levenberg-Marquardt (LM) algorithm to enhance convergence speed. Ten key parameters—including bench slope angle, geotechnical internal stress, bench height, surface displacement, and pore water pressure—were selected as inputs, with the slope safety factor as the output. Training and validation was performed using 150 field case datasets. The results show that GA-BP model reduces the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by 46.9%, 25.4%, and 5.38%, respectively, compared to the conventional BP model. Predictions are closer to the safety threshold (F_s = 1.2), indicating enhanced sensitivity and stability. Pearson correlation analysis confirms strong relationships between surface and internal displacement (0.98) and between pore water pressure and rainfall (0.75), supporting the rationality of the input indicators. The study demonstrates that GA-BP model effectively overcomes local optima and gradient vanishing issues in BP networks, providing a reliable tool for intelligent slope stability assessment.

Key words: genetic algorithm (GA), backpropagation (BP) neural network, open-pit mine, waste dumps, slope instability prediction, safety factor