# A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
## 3 Intelligent Application of Deep Learning in Tunnel Engineering
### Mountain Tunnel
#### 3.1.1 Rockburst Disaster
Therefore, employing data mining (DM) techniques can effectively address these issues, enhancing both the quality and quantity of the samples. [PERSON] et al. [50] applied rough set theory in data mining to establish a radial basis function neural network (GA-RBF). They successfully identified a complex non-linear mapping relationship between rockburst grade and its influencing factors. [PERSON] et al. [51] also employed data mining techniques to assess the probability of rockburst occurrences, along with their characteristics, such as type, location, depth, width, and time delay. In addition, combining over-the-top geological forecasting tools with deep network models (refer to Figure 6) significantly enhances the accuracy and efficiency of rockburst predictions. This method monitors the development and occurrence of rockbursts, utilizing rupture and noise waveforms from pre-processed monitoring signals as inputs to the deep neural network. This approach enables real-time detection and classification of signals, allowing for the quick identification of the source of monitoring signals. It not only forecasts geological information ahead of the tunnel face but also predicts potential disaster types, representing the most effective and accurate prediction method with superior generalization capabilities currently available. [PERSON] et al. [52] improved upon traditional advanced geological prediction methods, which often struggle with high recognition universality at lower implementation costs and reduced construction times. By integrating neural networks with drilling test technology, they devised a novel method for the intelligent analysis and identification of geological strata. The application of this method to advanced drilling data from the Jiudingshan Tunnel on the Chu-Da Expressway significantly reduced the error rate in stratum identification. [PERSON] et al. [53] introduced a new rockburst prediction approach using the TSP advanced geological prediction system combined with the initial geostress field obtained through RBF inversion. They emphasized that enhancing the accuracy of advanced geological prediction and in situ stress field inversion is crucial for improving rockburst prediction precision. Furthermore, leveraging microseismic monitoring technology, [PERSON] [54] conducted research using a deep convolutional neural network to classify microseismic waveforms of surrounding rock, focusing on noise reduction, array optimization, source localization, microseismic prediction, and rockburst warning. Building on these research findings, he established a database for the rockburst microseismic index and designed an intelligent rockburst warning platform, achieving automated monitoring of tunnel microseismic signals. Based on the above analysis, it can be found that the early warning, control, and monitoring of rockburst grade for deep rock mass engineering has become an urgent problem to be solved in major engineering construction in China and even in the world. The analysis of surrounding rock stress states, realized through the seismic wave signals generated by rock microfractures and obtained by microseismic monitoring technology, has been widely used worldwide in the monitoring and prediction of rockburst disasters, achieving great success [55]. With its great advantage in data processing, the deep learning algorithm can identify the key characteristics of the monitoring data for establishing the non-linear mapping relationship between the monitoring data and the source location. Therefore, researchers tend to use the deep learning algorithm to identify effective monitoring signals from the monitoring data to predict the subsequent rockburst risk. In addition, to avoid the  
Figure 6: Schematic diagram of the process of rockburst signal recognition [54]. influence of abnormal parameter values at certain times on the prediction results, determining the evolution trend of microseismic parameters over a period is also beneficial for rockburst prediction.