ViT-Based Image Regression Model for Shear-Strength Prediction of Transparent Soil

Abstract

The direct-shear test is the primary method used to test the shear strength of transparent soil, but this experiment is complex and easily influenced by experimental conditions. In order to simplify the process of obtaining the shear strength of transparent soil, an image regression model based on a vision transformer (ViT) is proposed in this paper; this is used to recognize the shear strength of the soil based on images of transparent-soil patches. This model uses a convolutional neural network (CNN) to decompose the transparent-soil images into multiple image patches containing high-order features, utilizes a ViT for feature extraction, and designs a regression network to facilitate the transfer of information between the abstract image features and shear strength. This model solves the problem of boundary blurring and difficult-to-identify features in speckle images. To demonstrate the effectiveness of the proposed model, different parameters related to transparent soil were obtained by controlling the particle size of fused quartz sand and the content of aerosol; in addition, the friction angle and cohesive force of the transparent soil under different proportions were measured using direct-shear tests, serving as two datasets. The results show that the proposed method achieves correlations of 0.93 and 0.94 in the two prediction tasks, thus outperforming existing deep learning models.