A stacked generalisation methodology for estimating the uniaxial compressive strength of rocks

Abstract

Uniaxial compressive strength (UCS) has become a highly essential strength parameter in the mining, civil and geomechanical industries. Estimating the exact value of the strength of rock has become a matter of great concern in real life. Despite this, there have been many works to indirectly/directly estimate the UCS of rocks. This study introduces a novel stacked generalisation methodology for estimating the UCS of rocks in geomechanics. In this study, generalised regression neural network (GRNN), radial basis function neural network (RBFNN), and random forest regression (RF) were used as the base learners and the multivariate adaptive regression spline (MARS) functioned as the meta-learner for the proposed stacking method. The proposed 3-Base learner stack model exhibited dominance over single applied AI methods of GRNN, RBFNN, and RF when confirmed with similar datasets by employing performance metrics like the Nash–Sutcliffe Efficiency Index (NSEI), Root Mean Squared Error (RMSE), Performance Index (PI), Scatter Index (SI) and Bayesian Information Criterion (BIC). The proposed 3-Base learner stack model scored the least RMSE, PI, and SI scores of 1.02775, 0.50691, and 0.00788 respectively for the testing datasets. In addition, it also produced the utmost NSEI value of 0.99969 and the least BIC value of 16.456 as likened to other competing models (GRNN, RBFNN and RF), reaffirming its power in forecasting the UCS of rocks in geomechanical engineering.