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<p>Investigating the effects of microfabric characteristics on the nondestructive parameters of rocks has always been one of the most challenging and hot topics in rock mechanics. This study focuses on how grain geometric characteristics affect Schmidt rebound hardness (SRH) and P-wave velocity (V<sub>P</sub>). For this purpose, nine granites were selected for measuring the Schmidt rebound hardness and P-wave velocity. Next, eight grain geometric characteristics including roundness, roughness, aspect ratio, rectangularity, convexity, concavity, circularity, and solidity were quantified using the image processing method. Simple and multiple regression analyses were used to link these grain geometric features with SRH and V<sub>P</sub>. The accuracy of the equations was checked using performance indices: coefficient of determination (R<sup>2</sup>), Nash-Sutcliffe Efficiency (NSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Scatter Index (SI). The simple regression analyses showed that circularity and aspect ratio have the greatest effect on SRH. Also, the circularity, roundness, and aspect ratio were found to be the most effective parameters on V<sub>P</sub>. Finally, the results of multiple regression analysis demonstrated that there are highly significant relationships between SRH and V<sub>P</sub> with the combination of grain shape characteristics, with R<sup>2</sup> of 0.999 and 0.988, respectively.</p>

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Effects of grain geometry on the nondestructive testing results of hard rock materials

  • 期刊论文

  • 发表日期：

    2025年11月24日

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  • Sasan Ghorbani,
  • Seyed Hadi Hoseinie

摘要

Investigating the effects of microfabric characteristics on the nondestructive parameters of rocks has always been one of the most challenging and hot topics in rock mechanics. This study focuses on how grain geometric characteristics affect Schmidt rebound hardness (SRH) and P-wave velocity (VP). For this purpose, nine granites were selected for measuring the Schmidt rebound hardness and P-wave velocity. Next, eight grain geometric characteristics including roundness, roughness, aspect ratio, rectangularity, convexity, concavity, circularity, and solidity were quantified using the image processing method. Simple and multiple regression analyses were used to link these grain geometric features with SRH and VP. The accuracy of the equations was checked using performance indices: coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Scatter Index (SI). The simple regression analyses showed that circularity and aspect ratio have the greatest effect on SRH. Also, the circularity, roundness, and aspect ratio were found to be the most effective parameters on VP. Finally, the results of multiple regression analysis demonstrated that there are highly significant relationships between SRH and VP with the combination of grain shape characteristics, with R2 of 0.999 and 0.988, respectively.