Artificial intelligence (AI) techniques for rock tunnel construction offer innovative solutions for assessing rock mass quality and ensuring excavation safety in challenging geological conditions. Both cutting-edge contact methods and noncontact methods such as digital photography can provide continuous geological data during excavation. Then, advanced deep learning algorithms for precise characterization of rock face features, along with pioneering multisource 3D data fusion modelling, can enable refined rock mass classification and sophisticated safety evaluation techniques tailored to complex geological environments.
By integrating machine vision and intelligent algorithms with rigorous statistical analysis and machine learning models, this book provides practical and refined solutions for the construction industry. It offers improved safety, efficiency, and reliability for tunnel projects and serves as a valuable reference for graduate students and academics.
Autorentext
Jiayao Chen is an associate professor at Beijing Jiaotong University. He is a board member and secretary of the Chinese Society of Civil Engineering Risk and Insurance Research Division.
Hongwei Huang is a distinguished professor at Tongji University, China, a "Yangtze Scholar," and President of the Engineering Risk and Insurance Research Branch of the Chinese Civil Engineering Society. He also leads the International Joint Research Center for Resilient Infrastructure.
Mingliang Zhou is an associate professor and assistant dean at the College of Civil Engineering, Tongji University and a recipient of the International Postdoctoral Exchange Fellowship. He earned his Ph.D. from Cambridge University, UK, and is a core member of ISSMGE's YMPG and TC309.