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摘要
In order to establish the nonlinear relationship between the optimization of slope state control parameters and the slope stability coefficient, a strength reduction(SR)-BP neural network optimization model for slope state control parameters was proposed to predict the slope stability under different slope state control parameters optimization schemes. Taking the high slope of a limestone open-pit mine in Huangshan as an example, the strength reduction method was used to calculate the slope stability coefficient under the scheme matrix of different slope state control parameters, and the sample data are obtained. An improved empirical formula of hidden layer node number was proposed to construct the parameter optimization model of BP neural network for slope state control. And then mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R) were used as performance evaluation indexes to analyze the relative error between actual sample value and model prediction value. The results show that, the influence of the number of nodes in input layer and output layer on the number of nodes in hidden layer is fully considered in the improved empirical formula; and the model of SR-BP neural network for slope state control parameter optimization expresses the nonlinear relationship between the optimization of slope state control parameters and the slope stability coefficient. The relative error between the actual sample value and the model prediction value is less than 6%, and MAE is 0.013, RMSE is 0.026, R is close to 1, which proves that the model fits well and the prediction accuracy is high. The research results can provide a certain guiding significance and theoretical basis for the preliminary design and optimization of mine slope control parameters. ? 2021, China Science Publishing & Media Ltd. All right reserved.
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