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郭延华,赵帅.基于KPCA-WOA-KELM的岩爆烈度预测[J].河北工程大学自然版,2021,38(2):1-7
基于KPCA-WOA-KELM的岩爆烈度预测
Classified Prediction Model of Rockburst Using KPCA-WOA-KELM
投稿时间:2021-02-27  
DOI:10.3969/j.issn.1673-9469.2021.02.001
中文关键词:  岩爆  核主成分分析  鲸鱼优化算法  核极限学习机
英文关键词:rockburst  kernel principal component analysis  Whale Optimization Algorithm  kernel-based extreme learning machine
基金项目:河北省自然科学基金资助项目(E2014402099)
作者单位
郭延华 河北工程大学 土木工程学院, 河北 邯郸 056038 
赵帅 河北工程大学 土木工程学院, 河北 邯郸 056038 
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中文摘要:
      岩爆是隧道开挖中常见的工程地质灾害,为准确预测岩爆烈度,提出基于KPCA-WOA-KELM的岩爆烈度预测模型。首先,根据岩爆烈度影响因素确定岩爆评判指标,并采用核主成分分析(KPCA)对岩爆数据做特征提取,简化模型输入参数的同时充分保留数据特征信息;其次,使用核极限学习机(KELM)拟合评判指标与岩爆烈度间的非线性映射关系,并采用鲸鱼优化算法(WOA)优化KELM的参数,避免人工设置参数对模型预测效果的影响;然后,使用准确率、精确率、召回率、F值等指标综合评估模型的预测性能;最后,利用秦岭终南山公路隧道岩爆实例验证该模型的可行性。研究表明,KPCA-WOA-KELM能有效地简化数据结构,避免局部最优解,提高岩爆烈度预测的准确率。
英文摘要:
      Rockburst is a common engineering geological disaster in deep rock excavation. In order to predict rockburst intensity grade accurately,this paper proposes a rockburst intensity prediction model based on KPCA-WOA-KELM. Firstly,rockburst evaluation indexes are determined according to the influencing factors of rockburst intensity,and the kernel principal component analysis (KPCA) is used to perform feature compression on rockburst data,so as to simplify the input data structure of the model and fully retain the data feature information. Secondly,the kernel-based extreme learning machine (KELM) was used to fit the nonlinear mapping relationship between the evaluation index and rockburst intensity,and the whale optimization algorithm (WOA) is used to optimize the parameters of KELM to reduce the impact of manual setting parameters on the model prediction effect. Then,the accuracy,precision,recall,F-measure and other indicators are used to evaluate the prediction performance of the model. Finally, the prediction of rock burst intensity of Zhongnanshan highway tunnel in Qinling Mountains is made to verify the feasibility and applicability of the model. The results show that KPCA-WOA-KELM can simplify the data structure more effectively,effectively avoid the local optimal solution,and improve the accuracy of rockburst intensity prediction.
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