Convolutional neural network-based model for recognizing TBM rock chip gradation.

A tunnel boring machine (TBM) generates rock chips during excavation, which are crucial for assessing surrounding rock integrity, enhancing excavation efficiency, and evaluating cutter wear. However, traditional methods struggle to identify small rock chips, chips submerged in soil or water, and chips in stacked states. This paper proposes a convolutional neural network (CNN)-based method for directly recognizing the particle size distribution from rock chip images. A dataset of 2520 rock chip images representing 84 particle-size distributions was collected in a laboratory environment. By comparing various CNN architectures and hyperparameters, an optimal model was obtained with a mean absolute error (MAE) of 1.66 × 10−2 and R 2 of 0.923 on the test set. The results demonstrate that the proposed method enables the real-time recognition of particle size distribution using rock chip images, which has the potential to significantly improve intelligent auxiliary excavation technology in TBMs. • A dataset of 2520 rock chip images representing 84 particle size distributions was collected. • A CNN-based method for directly recognizing particle size distribution from rock chip images is proposed. • An optimal model was obtained with a MAE of 1.66 × 10−2 and R 2 of 0.923 on the test set. [ABSTRACT FROM AUTHOR]