基于RBF神经网络的露天矿山边坡失稳预警方法  

Early Warning Method of Slope Instability of Open-pit Mine Based on RBF Neural Network

作　　者：谢振华[1] 梁莎莎[2] 张雪冬[2] 

机构地区：[1]中国劳动关系学院安全工程系,北京100048 [2]北京科技大学土木与环境工程学院,北京100083

出　　处：《金属矿山》2014年第9期7-10,共4页Metal Mine

基　　金："十二五"国家科技支撑计划重点项目(编号:2012BAK09B05)

摘　　要：边坡失稳研究是露天矿山安全生产的关键问题,边坡失稳智能化预警的实现是失稳研究的核心内容。以马钢南山铁矿凹山采场高陡边坡工程为例,建立了RBF神经网络预警模型,采用梯度下降训练算法并进行了改进,根据经验来设置算法的学习步长,选取黏聚力、内摩擦角,边坡角、边坡高度,孔隙水压力比、容重等6个因素作为网络的输入单元,利用所选的25组样本数据完成了RBF神经网络的学习。应用学习好的预警模型对南山铁矿凹山采场的2个帮进行了边坡失稳预警分析,得到2个帮的稳定性等级结果分别为1级和3级,即极稳定和基本稳定,与现场情况一致。该预警方法合理,具有推广应用价值。The slope instability has always been a key technical issue for the safe production in open-pit mine. The reali- zation of the intelligent warning of slope instability is the core of instability research. The early warning model based on RBF neural network was established,taking the high-steep slope engineering of Aoshan pit in Nanshan Iron Mine of Masteel as a case. Gradient descent algorithm for training was improved, and according to the experience, the algorithm＇s learning step was set up. Six factors of cohesion, internal friction angle, slope angle, slope height, ratio of pore water pressure and bulk density were selected as network input units. And,25 sets of sample data selected were used to complete the learning of RBF neural network. Then, the early warning model was used to make early warning analysis on instability of two slopes of Aoshan pit in Nanshan Iron Mine. Stability classification of the two slopes is respectively level 1 and level 3, that are, extremely stable and basically stable, which are accordant with the current actual situation. This early warning method is worth being applied and spread.

关 键 词：RBF神经网络 边坡失稳 高陡边坡 预警模型 

分 类 号：TD771[矿业工程—矿井通风与安全]