Prediction of mechanical strength based on deep learning using the scanning electron image of microscopic cemented paste backfill

Abstract

The mechanical strength of cemented backfill is an important indicator in mining filling. To study the nonlinear relationship between cemented paste backfill (CPB) and mechanical response, a deep learning technique is employed to establish the end-to-end mapping relationship between the scanning electron microscope (SEM) images and mechanical strength. A seven-layer convolution neural network is set up in the experiment, and the relationship between the SEM image and mechanical strength is established. In addition, the difference between the measured and predicted values is calculated and the mean and variance of the error are analyzed. The average accuracy of the mechanical strength prediction is found to be 8.28%. Thus, the proposed method provides a new technique for the quantitative analysis of mechanical strength of microscale CPB.