Assessment of the uniaxial compressive strength of intact rocks: an extended comparison between machine and advanced machine learning models - Khatti, Jitendra; Grover, Kamaldeep Singh (Multiscale and Multidisciplinary Modeling, Experiments and Design, 2024)
Rock strength is the most deterministic parameter for studying geological disasters in resource development and underground engineering construction. However, the experimental procedure for finding rock strength is arduous and lengthy. Therefore, this investigation introduces an optimal computational model for predicting the rock uniaxial compressive strength (UCS) by comparing eight machine learning approaches. For developing the predictive models, the selection of the most significant independent variables is essential. Hence, this investigation reveals the most suitable independent variable by developing three cases of input variables, i.e., (i) area, density, wave velocity, and Young's modulus; (ii) mass, density, wave velocity, and Young's modulus; and (iii) density, wave velocity, and Young's modulus. Sixteen performance metrics have analyzed machine learning models' prediction capabilities and reported that the Gaussian process regression (GPR) model has predicted rock UCS with a correlation coefficient (R) of 0.9788, root mean square error (RMSE) of 14.0804 MPa, performance index (PI) of 1.8821, variance accounted for (VAF) of 95.79, index of scatter (IOS) of 0.1167, and index of agreement (IOA) of 0.9063, close to the ideal values and higher than those of other computational models, in case 1. However, the impact of weak multicollinearity has been observed in the performance of the support vector machine model than GPR and ensemble tree models. The score analysis, error characteristics curve, and Anderson–Darling test confirm the robustness of assessing the rock UCS.