Prediction of intensity classification of rockburst based on deep neural network

Rockburst is one of the most critical problems in large-scale underground geotechnical engineering and deep mineral resource mining. In order to accurately and reliably predict rockburst disasters,a Dropout and improved Adam-based deep neural network (DA-DNN) rockburst prediction model is proposed. According to the affecting factors,characteristics,and genesis of the rockburst,this paper establishes a rockburst prediction index system composed of four evaluation indices,i.e.,tunnel-wall surrounding rock's maximum tangential stress,rock uniaxial compressive strength,rock uniaxial tensile strength,and rock elastic energy index. Based on the literature on rockburst,this paper has collected 289 groups of rockburst engineering case data and adopted them as the sample data for rockburst prediction. Then,the rockburst prediction model based on DA-DNN is established by deep learning technology. The DA-DNN model avoids the problem of the determination of index weights,takes a completely data-driven approach,weakens the influence of human factors,and is capable of learning complex and subtle deeper relationships in incomplete,imprecise,and noisy finite data sets. Considering the finiteness of the rockburst sample data,this paper,based on the common data set segmentation method used in the deep learning field,segments these data into training set,validation set,and test set by the ratio of 6:2:2. It randomly draws 58 groups from the sample data of Formation 289 as the test set (prediction samples),which is used to evaluate the generalization ability of the model after the completion of the final training and test its real prediction accuracy. The remaining 231 groups of learning sample data serve as the learning samples of the DA-DNN model. During training,80% of the learning samples are randomly drawn as the training set,while the remaining 20% are used as the validation set. When the number of training epochs is set as 60 and there are 231 groups of learning samples,the prediction accuracy of prediction samples reaches 98.3%. The following three engineering application cases,i.e.,Jinping II Hydropower Station,Qinling Tunnel,and Dongguashan Copper Mine,come from the prediction samples. The prediction results validate the effectiveness and correctness of the DA-DNN rockburst prediction model.