Prediction of Rock Compressive Strength Using Machine Learning Algorithms Based on Spectrum Analysis of Geological Hammer

Abstract

The traditional method to estimate rock compressive strength (RCS) in field operation is dependent on hammering rocks and artificial identification. It is too subjective to get high estimation accuracy. For this reason, the new and non-destructive method uses machine learning algorithms to analyze acoustic characteristics of geological hammer to predict RCS accurately. The hammering sound samples were successively preprocessed by signal enhancement algorithm and double-threshold method to reduce noise and acquire valuable intervals of all. We have also performed the time-frequency domain conversion on sound signal through FFT, which obtained two brand new indexes, amplitude attenuation coefficient and high and low frequency ratio, as the input parameters of models. By contrasting the performance of various models based on k-nearest neighbors, naive Bayes, random forest, artificial neural networks (ANN), and support vector machines (SVM), we uncovered that the prediction accuracy of both SVM and ANN was over 95%, superior to others. Thus, SVM and ANN were better for widespread application in geological surveys and construction acceptance to predict RCS accurately. In addition, characteristic mechanism of acoustic spectrum was explained from microstructure, energy dissipation and filter effect, which indicated why there existed strong correlation between acoustic characteristics and RCS. The current rock mass classification standard was supplemented with the above two characteristic indexes for better identification.