Research on strength identification of rock-like materials based on measured drilling parameters

In order to establish the relationship between drilling parameters of rock drill and rock strength, and carry out research on rock strength identification, an automatic collection system of drilling parameters by truck was built. In the test, three kinds of strength concrete, C30, C40, and C50, were selected to simulate the rock of the same strength, and different drilling parameters were dynamically collected. Four SVM classification models were constructed based on the Support Vector Machine (SVM) algorithm. The drilling data was trained and learned, and the kernel function coefficient was modified by optimization algorithm. The model was optimized according to classification accuracy and evaluation indicators. The results show that the accuracy of SVM model based on polynomial kernel and Gaussian kernel function can reach 90%, which can effectively identify the strength of rock-like materials.