# A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
###### Abstract  
With the advent of the era of big data and information technology, deep learning (DL) has become a hot trend in the research field of artificial intelligence (AI). The use of deep learning methods for parameter inversion, disease identification, detection, surrounding rock classification, disaster prediction, and other tunnel engineering problems has also become a new trend in recent years, both domestically and internationally. This paper briefly introduces the development process of deep learning. By reviewing a number of published papers on the application of deep learning in tunnel engineering over the past 20 years, this paper discusses the intelligent application of deep learning algorithms in tunnel engineering, including collapse risk assessment, water inrush prediction, crack identification, structural stability evaluation, and seepage erosion in mountain tunnels, urban subway tunnels, and subsea tunnels. Finally, it explores the future challenges and development prospects of deep learning in tunnel engineering. deep learning; tunnel engineering; mountain tunnels; urban subway tunnels; undersea tunnels; intelligent analysis +
Footnote †: journal: Medical  
Tunnel, the construction of strait passages in the Bohai Strait, the Qiongzhou Strait, and the Taiwan Strait is also underway. In China's \"13 th Five-Year Plan\", there are calls to strengthen the development and utilization of deep resources and the construction of a new pattern of underground urban space, which makes the geological conditions faced by tunnel construction more complex and further increases the construction difficulty. In the design and construction process of tunnel engineering, the geotechnical body, situated within the complex environments of engineering geology and hydrogeology, serves as the main research object. These environments are characterized by strong non-uniformity, non-determinism, and discontinuity. The excavation of the geotechnical body and its interaction with the surrounding geological environment, along with the excavation machinery, make the tunnel excavation process a dynamically changing one in both time and space [4]. At the same time, the adverse factors affecting tunnel construction are multi-scale and multi-level. The inherent complexity of the geotechnical body makes it challenging for researchers to directly determine these factors by monitoring data in an objective manner. Previously, predictions of these factors primarily relied on the expertise of field professionals and their engineering experience, without in-depth analysis. Therefore, accurately predicting and forecasting the stability of the construction process through simulation and analysis of the mechanical behaviors and phenomena during the excavation process has become a key research topic for many scholars [5; 6; 7; 8]. With the rapid development of computer technology, researchers have begun leveraging the powerful analytical capabilities of computers and artificial intelligence technology, combined with the fundamental theories of tunnel engineering, for the identification, study, and intelligent analysis of the mechanical behavior of rock bodies during excavation. Among these technologies, deep learning (DL), which integrates disciplines such as statistics, neuroscience, and computer science, stands out. This method, relying on data for learning and without the need for manual feature extraction, enables computers to build models that simulate human brain functions for information learning and processing. The application of deep learning in civil engineering began to expand gradually after [PERSON] first utilized the neural network method to address construction process issues in the late 1980s [9; 10]. [PERSON] was the first to apply neural network research to the ontological model of geotechnical materials. In the domestic civil engineering community, [PERSON] et al.