Research on Intelligent Control Technology for Surrounding Rock Disturbance in Hydraulic Shield Tunneling
- DOI
- 10.2991/978-94-6463-856-1_9How to use a DOI?
- Keywords
- shield tunneling; surrounding rock disturbance; intelligent monitoring; predictive control
- Abstract
With the widespread application of shield tunneling in large-scale hydropower projects, controlling surrounding rock disturbance has become a critical challenge. This study focuses on the development of an intelligent control framework that integrates multi-source sensing, real-time monitoring, machine learning–based prediction models, and adaptive feedback mechanisms. By analyzing disturbance mechanisms related to geological conditions, tunneling parameters, hydrogeology, and in-situ stress, the framework enables accurate perception and dynamic regulation of excavation-induced responses. Key technologies such as intelligent perception systems, ensemble learning, model predictive control (MPC), and particle swarm optimization are reviewed and applied. The results demonstrate improved tunnel stability, reduced environmental risk, and enhanced construction efficiency.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Hailong Li AU - Qinghua Xiao AU - Xiaofeng Xu PY - 2025 DA - 2025/09/22 TI - Research on Intelligent Control Technology for Surrounding Rock Disturbance in Hydraulic Shield Tunneling BT - Proceedings of the 2025 International Conference on Resilient City and Safety Engineering (ICRCSE 2025) PB - Atlantis Press SP - 80 EP - 86 SN - 2352-5401 UR - https://doi.org/10.2991/978-94-6463-856-1_9 DO - 10.2991/978-94-6463-856-1_9 ID - Li2025 ER -