Abstract: The application of an increasing number of new technologies in tunnel construction is a consequence of the rapid development of intelligent tunnel construction. Among these, the use of machine vision instead of human eyes for geological information recognition in tunnels has become increasingly widespread. The authors build on this trend by using machine vision to investigate intelligent classification methods for the lithology of the tunnel face. First, four types of lithology (limestone, mudstone, sandstone, and basalt) are screened and counted by collecting highdefinition tunnel face photos, geological sketches, and engineering geological descriptions along the highspeed railway. On this basis, image enhancement techniques are used to expand the number of samples and construct a lithology sample set. Then, based on the above sample sets, the ResNet50V2 lithology classification transfer learning model and the VIT lithology classification model are constructed. The lithology classification effects of the two models are compared, and the Stacking method is used to integrate the classification characteristics of the two models. The authors select the most suitable metalearner by comparing the integrated fusion effects of three metalearners, logistic regression(LR), support vector machine, and decision tree, on the two models. The results reveal that the Stacking model constructed by LR integrated with ResNet50V2 and VIT has the best classification effect on lithology. Apart from having an accuracy rate of 93.8%, it can also fully integrate the overall and local features of the lithology of the tunnel face for better classification.

Key words:

tunnel, lithology of tunnel face, convolutional neural network, visual Transformer, ensemble learning, Stacking method