基于IPSO-ELM模型的尾矿坝稳定性分析  

Analysis of tailings dam stability based on IPSO-ELM model

作　　者：赵允坤 胡军[1] 栾长庆 张瀚斗 ZHAO Yunkun;HU Jun;LUAN Changqing;ZHANG Handou(School of Civil Engineering,University of Science and Technology Liaoning,Anshan 114051,China;Beneficiation Branch,Gongchangling Mining Co.,Ltd.,Anshan Iron and Steel Group,Liaoyang 111008,China;Donganshan Sintering Plant,Angang Group Co.,Ltd.,Anshan 114041,China)

机构地区：[1]辽宁科技大学土木工程学院,辽宁鞍山114051 [2]鞍钢集团矿业弓长岭有限公司选矿分公司,辽宁辽阳111008 [3]鞍钢集团有限公司东鞍山烧结厂,辽宁鞍山114041

出　　处：《中国矿业》2022年第3期61-66,共6页China Mining Magazine

基　　金：辽宁省教育厅重点项目资助(编号:601009877-36)。

摘　　要：为了更准确地对尾矿坝稳定性进行预测,采用训练速度快、参数设置简单、准确度较高的极限学习机(ELM)模型,针对ELM模型在训练过程中随机产生的连接权值和隐含层阈值,导致泛化能力不足、模型稳定性差等问题,引入基于线性递减权重法改进的粒子群算法(IPSO)对其进行优化,提出了尾矿坝稳定性预测的改进粒子群优化极限学习机(IPSO-ELM)模型。将该模型运用到尾矿坝实例预测中,在选取的35组样本数据中,前30组作为训练样本,后5组作为测试样本,以内摩擦角、边坡角、尾矿坝材料重度、孔隙压力比、内聚力和边坡高度6个尾矿坝稳定性影响因素为输入参数,以尾矿坝稳定性安全系数为输出参数,将预测结果与ELM模型和PSO-ELM模型对比,结果表明,IPSO-ELM模型有较高的预测精度,预测值逼近于实际值,验证了IPSO-ELM模型在尾矿坝稳定性评价中的可靠性和有效性。In order to more accurately predict the stability of tailings dam,the training speed and simple parameters and high accuracy of extreme learning machine(ELM)model,in view of the ELM model randomly generated in the process of training the connection weights and threshold,the hidden layer in generalization ability insufficiency,the model of stability problems,introduced based on linear weighting method improved particle swarm optimization(IPSO)for its optimization,predicting the stability of tailings dam is proposed to improve the particle swarm optimization extreme learning machine(IPSO-ELM)model.To apply the model prediction in tailings dam instance,in the selection of 35 sample data,the first 30 group as the training sample,after 5 groups as the test sample,friction angle,slope angle,tailings dam materials within severe,pore pressure ratio,cohesion and the slope height 6 tailings dam stability influence factors as input parameters,in tailings dam stability safety factor for the output parameters,the prediction results and the ELM model and PSO-ELM model comparison,the results show that IPSO-ELM model has higher prediction precision,close to the actual and estimated values,the reliability and effectiveness of IPSO-ELM model in tailing dam stability evaluation are verified.

关 键 词：尾矿坝 稳定性 粒子群算法 极限学习机 线性递减权重法 

分 类 号：TD926.4[矿业工程—选矿]