Rock abrasiveness prediction based on multi-source physical, mechanical and mineralogical properties

Abstract

Abstract Rock abrasiveness is a vital parameter that affects cutter wear, tunneling efficiency, and cost budgeting during mechanical excavation. The Cerchar abrasivity index (CAI), a suggested standard parameter to characterize the rock abrasiveness, can be obtained through the laboratory test. Understanding the correlations between the CAI and physical, mechanical, and mineralogical properties helps to precisely evaluate the cutter wear and improve the excavation efficiency. In this study, 27 groups of rock samples collected from around the country were determined to establish the correlations between the CAI and 17 commonly used rock parameters using simple regression and multiregression. Based on PCC results, the possibility of linear relationships between CAI and 17 rock physical, mechanical, and mineralogical parameters was analyzed for determining the appropriate model. Subsequently, simple linear regression and Boltzmann models were developed based on physical and mechanical parameters, the model based on the porosity showed the excellent forecasting performance over other models. Through the analysis on the coefficient of determination (R2) value, a better multiregression model (R2=0.922) based on the mechanical parameters was obtained, but a more feasible model (R2=0.912) based on the thermal conductivity, diffusion coefficient, elastic modulus, and Rock Abrasivity Index (RAI) was also suggested after consideration of the simplicity and period of parameter measurement. After classification of rock types, the linear correlations strengthened significantly especially for the mineralogical properties, the CAI showed a linear correlation with equivalent quartz content (EQC) and RAI for the granite and sandstone, the quartz content (Q) still showed no relation with CAI.