Identification of the governing parameters related to rock indentation depth by using similarity analysis

This paper focuses on the indentation depth in rocks caused by a hemispherical indenter. The problem is approached by a combination of similarity methods with an artificial neural network. The similarity methods offer a profound understanding of the physical problem and help to identify the most important governing parameters or factors that reflect the essence of the rock indentation events, thus simplifying the target problem. The artificial neural network provides an advanced computing model, which allows more factors to be involved. The predictions obtained using this combined approach are in better agreement with the experimental results than predictions using other methods.