基于MIV-MA-KELM模型的岩爆烈度等级预测

MIV-MA-KELM model based prediction of rockburst intensity grade

为准确预测岩爆烈度等级以确保挖掘工程施工安全,提出一种基于MIV-MA-KELM的岩爆烈度等级预测模型。首先,在分析岩爆烈度影响因素的基础上确定主要评判指标,采用文化基因算法（MA）优化核极限学习机（KELM）参数,借助KELM拟合评判指标与岩爆烈度等级间的非线性映射关系;然后,利用平均影响值（MIV）方法以20%的调解率计算各指标影响权重,剔除低影响权重指标并反馈到MA-KELM模型中重新训练与测试;最后,选取巴玉隧道的68组数据进行试验,并用该模型预测秦岭隧道岩爆烈度等级。结果表明：预测结果与实际情况完全一致;MIV-MA-KELM模型能更合理地构建指标体系,有效避免局部最优解,提高岩爆烈度等级的预测准确率。

In order to evaluate the rockburst intensity grade accurately and ensure safety of excavation engineering,a classification model based on MIV-MA-KELM was built. Firstly,the mechanism of rockburst occurrence was used to analyze the factors influencing the intensity grade and further determine the main evaluation indicators. Then,the MA was introduced to carry out an optimum selection of Kernel-based KELM parameters,and KELM was used to fit the nonlinear mapping relationship between the influencing factors and rockburst intensity. Finally,the MIV was used to calculate the weight of each indicator at 20%mediation rate,and indicators having a low weight were removed and the others were fed back to MAKELM model for retraining and testing. According to the 68 sets of data from the Bayu tunnel,a series of comparative experiments were designed and carried out,and the model was applied to the prediction of rockburst intensity grade of Qinling Mountains tunnel. Experimental results show that the prediction results conform with the reality,and that the MIV-MA-KELM model can construct the indicators system more reasonably,and avoid local optimal solution,contributing to improving the classification accuracy.