Hard-rock tunnel lithology identification using multi-scale dilated convolutional attention network based on tunnel face images

Abstract

For real-time classification of rock-masses in hard-rock tunnels, quick determination of the rock lithology on the tunnel face during construction is essential. Motivated by current breakthroughs in artificial intelligence technology in machine vision, a new automatic detection approach for classifying tunnel lithology based on tunnel face images was developed. The method benefits from residual learning for training a deep convolutional neural network (DCNN), and a multi-scale dilated convolutional attention block is proposed. The block with different dilation rates can provide various receptive fields, and thus it can extract multi-scale features. Moreover, the attention mechanism is utilized to select the salient features adaptively and further improve the performance of the model. In this study, an initial image data set made up of photographs of tunnel faces consisting of basalt, granite, siltstone, and tuff was first collected. After classifying and enhancing the training, validation, and testing data sets, a new image data set was generated. A comparison of the experimental findings demonstrated that the suggested approach outperforms previous classifiers in terms of various indicators, including accuracy, precision, recall, F1-score, and computing time. Finally, a visualization analysis was performed to explain the process of the network in the classification of tunnel lithology through feature extraction. Overall, this study demonstrates the potential of using artificial intelligence methods for in situ rock lithology classification utilizing geological images of the tunnel face.

Graphical abstract

Keywords

hard-rock tunnel face / intelligent lithology identification / multi-scale dilated convolutional attention network / image classification / deep learning

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Acknowledgements

This research was funded by the National Natural Science Foundation of China (Grant No. 51978460) and the Open Fund of State Key Laboratory of Shield Machine and Boring Technology (No. SKLST-2019-K08), which are gratefully acknowledged.

Conflict of Interest

The authors declare that they have no conflict of interest.

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