基于BKA-CNN-SVM模型的岩爆烈度预测  

Prediction of Rockburst Grade Based on BKA-CNN-SVM Model

作　　者：慕慧文 周宗红[1] 郑发萍 刘剑 曾顺洪 段勇 MU Huiwen;ZHOU Zonghong;ZHENG Faping;LIU Jian;ZENG Shunhong;DUAN Yong(Faculty of Land Resources Engineering,Kunming University of Science and Technology,Kunming 650093,Yunnan,China;School of Public Safety and Emergency Management,Kunming University of Science and Technology,Kunming 650093,Yunnan,China;Yunnan Yuntianhua Polyphosphorus New Materials Co.,Ltd.,Zhaotong 657200,Yunnan,China)

机构地区：[1]昆明理工大学国土资源工程学院,云南昆明650093 [2]昆明理工大学公共安全与应急管理学院,云南昆明650093 [3]云南云天化聚磷新材料有限公司,云南昭通657200

出　　处：《高压物理学报》2025年第5期103-116,共14页Chinese Journal of High Pressure Physics

基　　金：国家自然科学基金(52264019,51864023);云南省基础研究计划项目青年项目(202401AU070175)。

摘　　要：为实现准确高效的岩爆烈度预测,做好地下工程灾害防治,提出了一种基于黑翅鸢优化算法-卷积神经网络-支持向量机(BKA-CNN-SVM)的岩爆烈度预测模型。首先,根据岩爆烈度的影响因素,确立6个主要岩爆预测指标,搜集国内外284组岩爆案例,建立岩爆数据库;然后,引入拉依达准则与1.5倍四分位差对数据进行异常值剔除及替换;接着,采用核主成分分析,对数据进行降维及特征提取,并将所提取的特征作为模型输入;最后,通过引入混淆矩阵,结合准确率、精确率、F_(1)值、召回率对模型性能进行评估,并与卷积神经网络(CNN)模型、极限学习机(ELM)模型、卷积神经网络与支持向量机(CNN-SVM)集成模型的性能进行对比。结果表明:BKA-CNN-SVM模型的准确率、精确率、F_(1)值、召回率分别达到95.35%、0.89、0.92、0.94,在预测精度和泛化程度上均明显优于其他模型。采用该模型预测锦屏二级水电站岩爆烈度,结果显示,预测结果与现场情况有较高的一致性。研究结果可为岩爆等级预测提供新方法。In order to realize efficient and accurate rockburst grade prediction,and prevent underground engineering disasters,this paper proposes a prediction model based on black-winged kite optimization algorithm-convolutional neural network-support vector machine(BKA-CNN-SVM).Firstly,the prediction index system was established according to six influence factors of rockburst,and 284 groups of rockburst cases at home and abroad were collected to establish a rockburst database.Secondly,Laida criterion and 1.5 times quartile difference were introduced to remove and replace the outliers in the data.The kernel principal component analysis(KPCA)was used to reduce the dimension of the data and extract the features.The extracted features were used as the model inputs.Finally,the confusion matrix was used to evaluate the model performance in terms of accuracy,precision,recall,and F1 value.BKA-CNN-SVM model was compared with convolutional neural network(CNN)model,extreme learning machine(ELM)model,and convolutional neural network and support vector machine(CNN-SVM)integrated model.The results showed that the accuracy,precision,F1 value,and recall of BKA-CNN-SVM model are 95.35%,0.89,0.92,and 0.94,respectively,which are significantly better than the other models in terms of prediction accuracy and generalization degree.In order to verify the feasibility of the BKA-CNN-SVM model,it was used to prediction the rockburst grade of the Jinping secondary hydro-power station.The prediction results have high consistency with the actual field conditions.This research can provides a new method for rockburst grade prediction.

关 键 词：岩爆 核主成分分析 卷积神经网络 支持向量机 黑翅鸢优化算法 

分 类 号：O347[理学—固体力学] TU45[理学—力学] O521.9[建筑科学—岩土工程]