基于灰色关联分析和GRU模型的岩爆等级预测  

Rockburst Grade Prediction Based on Grey Correlation Analysis and GRU Model

作　　者：满轲 武立文 刘晓丽[2] 宋志飞 李可娜 Man Ke;Wu Liwen;Liu Xiaoli;Song Zhifei;Li Kena(College of Civil Engineering,North China University of Technology,Beijing 100144,P.R.China;State Key Laboratory of Hydroscience and Hydraulic Engineering,Tsinghua University,Beijing 100084,P.R.China)

机构地区：[1]北方工业大学土木工程学院,北京100144 [2]清华大学水沙科学与水利水电工程国家重点实验室,北京100084

出　　处：《地下空间与工程学报》2025年第2期695-708,719,共15页Chinese Journal of Underground Space and Engineering

基　　金：国家重点研发项目(2018YFC1504801,2018YFC1504902);清华大学水沙科学与水利水电工程国家重点实验室资助(2019-KY-03);北方工业大学毓杰项目(216051360020XN199/006)。

摘　　要：岩爆是地下工程中常见的一种动力地质灾害。为提高岩爆等级的预测效果及预测模型的泛化性,提出了一种具有优良记忆性和强时序性的门控循环单元(Gate Recurrent Unit,GRU)神经网络模型预测岩爆等级。首先,综合岩爆成因特点并基于灰色关联分析筛选出合理的岩爆预测指标,采用已有研究成果的137组岩爆样本数据作为输入和输出数据集。其次,通过模型的超参数调整以确定最佳超参数和最佳预测效果,将GRU模型的岩爆等级预测效果与RF模型、SVM模型、CNN模型、LSTM模型、BP模型、Russenes判据、王元汉判据、关宝树判据、脆性系数判据和弹性能指数判据的岩爆等级预测效果进行比较以验证GRU模型的有效性,并根据不同模型的随机采样分析检验GRU模型的泛化性。最后,采用2个工程实例验证GRU模型的实用性。结果表明:合理的岩爆预测指标为围岩切向应力σθ、应力系数σθ/σc、压拉比σc/σt和弹性能指数Wet;GRU模型预测岩爆等级的效果明显高于上述其他预测方法;根据不同模型的随机采样分析结果可知,GRU模型预测岩爆等级的泛化性强;2个工程实例的岩爆等级预测结果与实际岩爆情况比较符合,本文所提出的GRU模型具有实用性。Rockburst is a common dynamic geological hazard in underground engineering.In order to improve the prediction effect of the rockburst grade and the generalization of the prediction model,the Gate Recurrent Unit(GRU)neural network model with excellent memory and strong temporal timing was proposed to predict the rockburst grade.Firstly,based on the characteristics of rockburst genesis and grey correlation analysis,reasonable rockburst prediction indicators were screened,and the data of 137 sets of rockburst samples with existing research results were used as input and output data sets.Then,the optimal hyperparameter and the best prediction effect were through hyperparameter tuning of the model,and the rockburst grade prediction effect of the GRU model was compared with the rockburst grade prediction effect of the RF model,SVM model,CNN model,LSTM model,BP model,Russenes criterion,Wang Yuanhan criterion,Guan Baoshu criterion,brittleness coefficient criterion and elastic energy index criterion to verify the effectiveness of the GRU model,and verify the generalization of the GRU model according to the random sampling analysis of different models.Finally,two engineering examples were used to verify the practicality of the GRU model.The results show that the reasonable prediction indicators of rockburst are tangential stress of surrounding rock σ_(θ),stress coefficient σ_(θ)/σ_(c),the ratio of uniaxial compressive strength to uniaxial tensile strength σ_(c)/σ_(t) and elastic energy index W_(et).According to the result analysis of different prediction methods,the effect of the GRU model in predicting rockburst grade is significantly higher than the above other prediction methods.Moreover,according to the random sampling analysis results of different models,the generalization of the GRU model in predicting rockburst grade is significantly strong.The prediction results of rockburst grade in the two engineering examples are in line with the actual rockburst situation,and the GRU model proposed in this paper is pract

关 键 词：岩爆等级预测 GRU模型 灰色关联分析 随机采样分析 

分 类 号：U455[建筑科学—桥梁与隧道工程]