A numerical integrated approach for the estimation of the uniaxial compression strength of rock from point load tests

The point load test (PLT) has been considered a flexible approach to estimate the uniaxial compressive strength (UCS) of rocks. Previously, empirical equations were obtained by mathematical fitting or machine learning to predict the UCS of rocks. The acquisition of these equations usually required a large amount of experimental data, while the corresponding parameters often lacked clear physical meanings, and the applicability of these empirical equations was limited. In this work, we attempted to develop a new method to predict the UCS of rocks by using the concept of a digital twin (numerical modelling). First, an automatic calibration procedure was used to obtain the numerical parameters of a digital twin for the PLT. Next, the UCS was predicted numerically by using a digital twin of the UCS test with the calibrated parameters. We performed a comprehensive comparison of our proposed method with previously obtained empirical equations and showed the superiority of our approach in better predicting the UCS of rocks. In this paper, we also discuss the influence of particle size and heterogeneity of rock material to illustrate the possible merits of the proposed method. Our work also shows the possible benefits of integrating numerical modelling into physical experimental tests of rocks.