Abstract: Onsite determination of the weathering degree of the surrounding rock has disadvantages such as strong subjectivity and poor accuracy. Therefore, a quantitative identification technology based on image recognition and machine learning is developed. Based on the images of the tunnel working surface collected from the DaqingGuangzhou doubleline expansion project, the digital image processing technology is applied to automatically extract the color, texture, and joint information of the tunneling face. Next, a model for determining the weathering degree of the surrounding rock is established by applying machine learning to filter the extracted parameters. Conclusions drawn are as follows: (1) The indices contributing to the accuracy of the prediction model from large to small are color, integrity, and texture, corresponding to the prediction accuracies of 71.91%, 70.78%, and 47.19%. (2) Compared with the single index prediction model, the multiindex prediction model has a higher prediction accuracy. The prediction model with three indices of color, texture, and integrity exhibits a prediction accuracy of 86.52%. (3) Eight key secondary indices are filtered from 13 indices, based on which a new prediction model is constructed with a prediction accuracy of 85%, meeting the engineering requirements. Results show that color, texture, and integrity information can effectively represent the weathering degree of the surrounding rock. Additionally, the prediction model based on these indices can stably and effectively determine the weathering degree of the tunneling face.

Key words: tunnel engineering, weathering degree of surrounding rock, machine learning, digital image processing