A Kernel Extreme Learning Machine-Grey Wolf Optimizer (KELM-GWO) Model to Predict Uniaxial Compressive Strength of Rock

Uniaxial compressive strength (UCS) is one of the most important parameters to characterize rock mass in geotechnical engineering design and construction. In this study, a novel kernel extreme learning machine-grey wolf optimizer (KELM-GWO) model was proposed predict UCS 271 samples. Four namely porosity (Pn, %), Schmidt hardness rebound number (SHR), P-wave velocity (Vp, km/s), point load (PLS, MPa) were considered as input variables, output variable. To verify effectiveness accuracy KELM-GWO model, machine (ELM), KELM, deep (DELM) back-propagation neural network (BPNN), empirical established compared with UCS. The root mean square error (RMSE), determination coefficient (R2), absolute (MAE), prediction (U1), quality (U2), variance accounted for (VAF) adopted evaluate all models study. results demonstrate that best predicting performance indices. Additionally, identified parameter by using impact value (MIV) technique.