Application of a Hybrid Machine Learning Model for the Prediction of Compressive Strength and Elastic Modulus of Rocks

Abstract

This paper presents a machine learning-based approach to estimating the compressive strength and elastic modulus of rocks. A hybrid model, GWO-ELM, was built based on an extreme learning machine network optimized by the grey wolf algorithm. The proposed model was carried out on 101 experimental datasets, and four commonly used models were used as benchmarks to evaluate the accuracy of the proposed model. The results showed that the proposed hybrid model can accurately achieve the prediction of elastic modulus and compressive strength with high correlation coefficients and small prediction errors. The prediction performance of the hybrid model is significantly better than the other four original models, and it is an alternative model for predicting the compressive strength and elastic modulus of rocks, which is recommended as an auxiliary tool for real-time prediction of rock mechanical properties.