Experimental study on rapid prediction of uniaxial compressive strength of rock based on vibrating signals of raw rock with drilling

In order to reveal the response relationship between the vibrating signals with drilling and the uniaxial compressive strength(UCS) of rock and to realize the rapid sensory prediction of the UCS, a hybrid genetic algorithm optimization (GA-BP) artificial neural network rapid prediction method for the UCS is proposed based on the vibrating signals with drilling. The Fourier transform and mathematical operations are used to extract the eigenvalues of the vibrating signals of granite, limestone, shale, sandstone and coal in the time and frequency domains to establish different neural network prediction models and to analyze and compare the prediction performance of each model. The results show that the coefficient of determination R² of the GA-BP neural network model optimized by the genetic algorithm for the training set is 0.778, which is improved by 9.4% compared with that by the BP neural network model. The proposed model has a good prediction capability for the UCS, and the method used provides a new technological path for the development of intelligent and automated techniques for rapid