Development of a real-time muck analysis system for assistant intelligence TBM tunnelling. (January 2021)

• Record Type: Journal Article
• Authors: Gong, Qiuming; Zhou, Xiaoxiong; Liu, Yongqiang; Han, Bei; Yin, Lijun
• Is Part Of: Tunnelling and underground space technology. Volume 107(2021)
• Publication Date: 2021-01
• Subjects: Tunnel boring machine -- Assistant intelligence tunnelling -- Machine vision -- Deep learning -- System design; Tunneling -- Periodicals; Underground construction -- Periodicals; Tunnels -- Periodicals; Underground areas -- Periodicals; 624.193
• DOI: 10.1016/j.tust.2020.103655
• Languages: English
• ISSNs: 0886-7798

Abstract

Highlights: A muck analysis system for assistant intelligence TBM tunneling was developed. The muck mass flow, volume flow and images in the belt were monitored in real time. A series of deep learning algorithms were programmed to analyze these obtained data. The system was applied to predict the rock mass condition and alarm the instability. The system was also applied to optimize the TBM operation parameters. 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.