基于BP神经网络的露天边坡角预测

针对广东泥竹塘铁矿露天边坡稳定性问题,为了获得矿山的稳定露天边坡角,研究使用BP神经网络进行预测分析。在预测过程中,以边坡岩体质量系数、岩体综合抗压、抗拉强度、内聚力、结构面力学特性、边坡高度及岩体密度等7个指标为输入因子,综合国内矿山27组露天矿山现场数据,建立网络学习、训练样本库,从而实现泥竹塘铁矿稳定露天边坡角的预测。结果表明,露天边坡角的BP神经网络预测模型最大误差小于3%,训练输出误差较小,精度较高,得到的泥竹塘露天铁矿上盘最终边坡角的预测值为42.8°,上盘最终边坡角的预测值为40.1°。多年的生产实践表明,该预测成果与实际基本相符,可为今后类似工程提供参考。

Prediction of slope angle based on BP neural network

Aiming at solving the stability problem of slope in Guangdong Nizhutang Iron Mine, this paper uses BP neural network prediction analysis to obtain the stability slope angle. In the prediction process, the slope rock mass quality coefficient, comprehensive compressive strength, tensile strength, cohesion, surface characteristics, slope height and mass density are chosen as input factors, and comprehensively consider the field data of 27 groups domestic mine open-pit mine. A BP neural network is established to predict the stability slope angle in Nizhutang Iron Mine. The results show that the maximum error of prediction model is less than 3%, which means that the training output error of BP neural network is small. The value slope angle of hanging side in Nizhutang Open-pit Mine is forecasted to be 42.8 degrees, the value angle of ultimate slope is 40.1 degrees. Many years of production practice shows that the forecast results are basically consistent with the actual situation, and the results can provide reference for similar projects.