(1. 武漢科技大學 資源與環(huán)境工程學院，武漢 430081；
2. 冶金礦產(chǎn)資源高效利用與造塊湖北省重點實驗室，武漢 430081)
摘 要: 為建立邊坡坡態(tài)控制參數(shù)優(yōu)化與邊坡穩(wěn)定性系數(shù)之間的非線性關系，提出SR-BP神經(jīng)網(wǎng)絡坡態(tài)控制參數(shù)優(yōu)化模型，預測不同坡態(tài)控制參數(shù)優(yōu)化方案下的邊坡穩(wěn)定性。以黃山某石灰石露天礦高邊坡為例，采用強度折減法，計算不同坡態(tài)控制參數(shù)方案矩陣下的邊坡穩(wěn)定性系數(shù)，獲得樣本數(shù)據(jù)，提出改進的隱含層節(jié)點數(shù)求解經(jīng)驗公式，構(gòu)建SR-BP神經(jīng)網(wǎng)絡坡態(tài)控制參數(shù)優(yōu)化模型，并將平均絕對誤差(MAE)、均方根誤差(RMSE)以及相關系數(shù)(R)作為性能評價指標，分析實際樣本值與模型預測值的相對誤差。結(jié)果表明：改進的隱含層節(jié)點數(shù)求解經(jīng)驗公式充分考慮了輸入層和輸出層節(jié)點數(shù)對隱含層節(jié)點數(shù)的影響；SR-BP神經(jīng)網(wǎng)絡坡態(tài)控制參數(shù)優(yōu)化模型表達了坡坡態(tài)控制參數(shù)優(yōu)化與邊坡穩(wěn)定性系數(shù)之間的非線性關系，其實際樣本值與模型預測值相對誤差均在6%以內(nèi)，且MAE為0.013，RMSE為0.026，R接近于1，證明模型擬合較好，預測精度較高。研究成果可為礦山坡態(tài)控制參數(shù)初步設計及優(yōu)化提供一定的指導意義及理論基礎。
關鍵字: 安全工程；坡態(tài)控制參數(shù)；穩(wěn)定性系數(shù)；強度折減法(SR)；BP神經(jīng)網(wǎng)絡
（1. School of Resources and Environmental Engineering, Wuhan University of Science and Technology, Wuhan 430081, China;
2. Hubei Key Laboratory for Efficient Utilization and Agglomeration of Metallurgic Mineral Resources, Wuhan 430081, China）
Abstract: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.
Key words: safety engineering; slope control parameters; stability coefficient; strength reduction method(SR); BP neural network
