A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Tunneling and Rock Mechanics · Rock Mechanics and Modeling · Geotechnical Engineering and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China
China Railway Construction Bridge Engineering Bureau Group Co., Ltd., Tianjin 300300, China
School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Shaanxi Key Laboratory of Geotechnical and Underground Space Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Funding
the Innovation Capability Support Plan of Shaanxi Province-Innovation Team · the Natural Science Foundation of Shaanxi Province
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