Yongcan Chen, Peng Lin, Guojin Zhu, Xiang Yan, Zhilin Wang, Rongfeng Mou, Zhiyun Deng, Huimin Xie, Mengzhe Cai, Haoran Wang
With the rapid expansion of hydropower projects, the safety and stability of hydraulic tunnels have become critical challenges. Traditional deformation monitoring and disaster prevention methods are insufficient for addressing the growing lengths of tunnels, large diameters, high water pressures, and complex geological conditions. An investigation of technologies designed to enhance the construction, operation, and maintenance of tunnels in complex environments has been systematically conducted. The research addresses three primary aspects: (1) the coordinated deformation mechanisms and failure modes of the rock-lining system, in which a novel nonlinear reinforcement program has been developed to optimize support design and reduce unnecessary reinforcement; (2) the improvement of intelligent monitoring systems, where multisensor fusion and artificial intelligence (AI)-based predictive analysis have been integrated to enhance real-time data acquisition and feedback accuracy; (3) the advancement of intelligent disaster prevention through robotic inspection technologies and digital twin (DT)-based risk assessment. The integration of emerging technologies, such as machine learning, automation, and real-time structural monitoring, significantly enhances tunnel stability and operational efficiency. Despite these advancements, further research is needed to refine multiscale modeling techniques, enhance AI-driven monitoring systems, and develop more adaptive robotic inspection solutions to ensure the long-term safety and sustainability of tunnels.