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

Abstract

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.