Abstract: To fully leverage the advantages of mechanization and informatization in tunnel boring machine (TBM) operations, the authors aim to promote the advancement of tunnel construction technology toward intelligent development. This involved exploring the deep integration of next-generation artificial intelligence technologies, such as sensing technology, automatic control technology, big data technology, deep learning, and machine vision, with key operational processes,including TBM excavation, direction adjustment, step changes, inverted arch block assembly, material transportation, and operation status assurance. The results of this integration are summarized as follows. (1) TBM key excavation parameter prediction algorithm was developed with an accuracy rate exceeding 90%. The TBM intelligent step-change control algorithm, based on machine vision, achieved an image segmentation accuracy rate of 95% and gripper shoe positioning error of ± 5 mm. (2) An automatic positioning system for inverted arch blocks was developed, enabling real-time perception of the spatial position and deviation during the assembly process. The system maintains an elevation positioning deviation within ± 3 mm and a horizontal positioning deviation within ± 10 mm, reducing the number of surveyors in each work team. (3) A TBM intelligent rail transportation system that achieves real-time human-machine positioning, automatic switch opening and closing, automatic obstacle avoidance, intelligent transportation planning, and integrated scheduling and command was designed. Each locomotive formation reduces one shunter and improves comprehensive transportation efficiency by more than 20%. (4) Intelligent analysis and prediction algorithms were developed to monitor and predict the trends of the hydraulic and gear oil parameters in real time, enhancing the proactive maintenance and system reliability.

Key words: tunnel, open TBM, intelligent construction, deep learning, machine vision