Evaluation method for the intactness of tunnel face surrounding rock based on tunnel face images.

The intactness of rock masses is a fundamental parameter in classifying surrounding rocks. Due to limitations imposed by the extent of tunnel face outcrops, the assessment of rock mass intactness necessitates the manual extraction of the positions, orientations and spacing of joints/fissures. To mitigate the labour-intensive nature of this process, in this paper deep learning is employed to develop an integrated method for the automated extraction of joints/fissures and the quantitative analysis of rock mass intactness. We introduce an image preprocessing method based on multiscale histogram equalization to obtain high-contrast, low-noise images. The DeepIntactness model, which incorporates the strategy of curriculum learning to utilize a large number of unlabelled tunnel rock images for model training, is introduced for the extraction of joints/fissures. Following the extraction of joints/fissures, a multiline centre statistic method based on the rock mass block index method is employed to evaluate the intactness of the most vulnerable part of the tunnel face. By applying this approach to an engineering structure, its capacity to automatically extract and quantitatively evaluate the engineering properties of the surrounding rock mass intactness is demonstrated. Hence, this method provides a novel approach to evaluating the tunnel surrounding rock intactness using two-dimensional images. Supplementary material: The I-RBI calculation process of the cases in this paper is available at https://doi.org/10.6084/m9.figshare.c.7154677 [ABSTRACT FROM AUTHOR]