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01-10-2024 | Original Paper

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

Authors: Yun Wu, Long-Chuan Deng, Xiao-Zhao Li, Li-Yuan Yu, Jiang-Feng Liu, Jian Lin

Published in: Bulletin of Engineering Geology and the Environment | Issue 10/2024

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Abstract

Rock abrasiveness is a vital parameter affecting 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 paper, correlations between CAI and 17 commonly used rock parameters were established for 27 groups of rock samples collected from China using simple and multiple regression methods. Based on the Pearson correlation coefficient (PCC) results, the possibility of linear relationships between CAI and physical, mechanical, and mineralogical parameters of rock samples 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 porosity showed excellent forecasting performance over other models. Through the analysis on the coefficient of determination (R2) value, a better multiregression model (R2 = 0.92) based on the mechanical parameters was obtained. However, a more feasible model (R2 = 0.91) based on the thermal conductivity, diffusion coefficient, elastic modulus, and Rock Abrasivity Index (RAI) was also suggested, considering the simplicity and period of parameter measurement. After the 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, while the quartz content (Q) still showed no relation with CAI. The results can provide a reference for evaluating the abrasive properties of rock during the mechanical excavation process.

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Metadata
Title
Rock abrasiveness prediction based on multi-source physical, mechanical and mineralogical properties
Authors
Yun Wu
Long-Chuan Deng
Xiao-Zhao Li
Li-Yuan Yu
Jiang-Feng Liu
Jian Lin
Publication date
01-10-2024
Publisher
Springer Berlin Heidelberg
Published in
Bulletin of Engineering Geology and the Environment / Issue 10/2024
Print ISSN: 1435-9529
Electronic ISSN: 1435-9537
DOI
https://doi.org/10.1007/s10064-024-03886-5