Development of a real-time muck analysis system for assistant intelligence TBM tunnelling

Intelligent tunnelling has become an important direction for the development of TBM technology recently. As a result of the interaction between rock mass and TBM cutterhead, mucks are very important for predicting rock mass conditions and evaluating rock breaking efficiency. A real-time muck analysis system for assistant intelligence TBM tunnelling is proposed in this paper. Machine vision was applied to take the muck images continuously in the high-speed conveyor belt. The image segmentation and feature extraction of the mucks are conducted by using a deep learning algorithm. The proposed system also measured the mass and volume flow of the muck by installing a belt scale and a scanner to monitor the stability of the rock mass on the tunnel face. After the system was completed, it was installed on an indoor simulation experimental platform. A series of experiments were conducted to verify the design functions and measurement accuracy. Additionally, the system was applied to a TBM tunnelling project. The application results showed that the proposed system reached its design requirements and functions, and can provide muck data support for further assistant intelligent TBM tunnelling.

Automated summary

An intelligent tunnelling system for real-time muck analysis was proposed in this paper, utilizing machine vision and deep learning algorithm to continuously monitor the muck condition during TBM tunnelling, as well as measure the mass and volume flow of the muck, ultimately improving the efficiency of tunnel excavation.