# A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
###### Abstract
[11] were among the early researchers engaged in neural network studies, using artificial neural networks to analyze the relationship between the central intensity of earthquakes and their corresponding magnitudes, and addressing the issue of sample non-convergence with a filtering method. However, in actual tunnel engineering problem analyses, despite more advanced data acquisition methods and limitations imposed by the engineering environment, the feature information of the excavation face or the deformation data of the supporting structure are not effectively utilized [12], and the majority of data end up archived after project completion [13], rendering them insufficient for the full utilization of deep learning methods for analysis. Current research indicates that data mining is the primary means of addressing such issues, with many scholars already employing this technology for research on engineering problems like geologic disaster prevention and control [14], geologic advance prediction [15], and structural deformation prediction [16]. With ongoing innovations in computer technology and continuous advancements in deep learning theory, the deep learning method based on large data samples holds broad research and application prospects in tunnel engineering. In addition, artificial intelligence and a series of intelligent products differ from the human brain in their inability to produce abstract or image thinking similar to that of the human brain. They also lack the cognitive process of understanding things inherent to the human brain. This cognitive process often stems from common sense, which includes both physical common sense and the common sense used in daily life. On the other hand, the thinking process of artificial intelligence is derived from its technological basis, namely, the chip. However, when the available data are insufficient or inaccurate, this can lead to issues in the computational processes of these products. Yet, true intelligent analysis involvesunderstanding the essence of the subject matter from a limited amount of data. The ability to change one's thinking plays a crucial role here, leveraging the advantages of human brain thinking, including abstract and image thinking, to move from understanding the essence of things to establishing intelligent thought processes and ultimately arriving at the final analysis results. Through the China National Knowledge Infrastructure (CNKI) and Web of Science, among other literature search engines, a total of 1068 Chinese and English articles published over the past 20 years on the topics of \"deep learning\", \"neural network\", and \"tunnel engineering\" were selectively reviewed. The applications of deep learning methods in tunnel engineering research areas have been categorized into 10 themes, as depicted in Figure 1. It is observed that the most extensive research has been conducted in the area of \"tunnel disease identification and assessment\". This primarily includes the identification of lining cracks and water leakage areas [17; 18], the prediction of rockburst and collapse risk [19; 20], and the control of surrounding rock deformation [21], among other factors. [PERSON] et al. [22] combined the mean impact value algorithm (MIV-A) with the improved firefly algorithm to optimize the probabilistic neural network (PNN), using cumulative microseismic event numbers, microseismic energy, etc., as input parameters and the rockburst intensity level as the output parameter. They proposed a rockburst prediction method based on microseismic monitoring and an optimized probabilistic model, demonstrating that the prediction rate of rockburst based on this method can reach 86.75%. [PERSON] et al. [23] integrated the empirical modal decomposition method with long short-term memory (LSTM) [24] to create a deep learning model capable of adaptively processing noisy data.