Investigating the Accuracy of Specimen Shape for Point Load Index Test in Predicting the Uniaxial Compressive Strength for Rocks Using Regression Analysis and Machine Learning - Mining, Metallurgy & Exploration (2023)

Society for Mining, Metallurgy & Exploration
Deniz Akbay
Organization:
Society for Mining, Metallurgy & Exploration
Pages:
9
File Size:
1464 KB
Publication Date:
Oct 18, 2023

Abstract

The strength of rocks and soil is a crucial design parameter in engineering projects, and it can be determined through various test methods such as uniaxial compressive strength, tensile strength, and shear strength. The point load index test is a popular indirect test method for predicting the uniaxial compressive strength of rocks. However, the reliability of the point load index’s estimation of uniaxial compressive strength of rocks is questioned due to the wide range of coefficients that are used to predict uniaxial compressive strength of rocks using point load index values. Factors such as the shape and type of rock specimen, practitioner, and test apparatus used can affect the accuracy of the point load index test. This study investigated the effect of four different specimen shapes used in point load index test (diametral, axial, block, and irregular lump tests) in predicting the uniaxial compressive strength. The rock samples were tested using four test procedures which are called diametral, axial, block, and irregular lump tests. The results showed that the irregular lump test was the most accurate in predicting uniaxial compressive strength, with the highest correlation coefficients and lowest mean absolute percentage errors. The point load index test can be used as a reliable predictor of uniaxial compressive strength of rocks when the irregular lump test is preferred.
Citation

APA: Deniz Akbay  (2023)  Investigating the Accuracy of Specimen Shape for Point Load Index Test in Predicting the Uniaxial Compressive Strength for Rocks Using Regression Analysis and Machine Learning - Mining, Metallurgy & Exploration (2023)

MLA: Deniz Akbay Investigating the Accuracy of Specimen Shape for Point Load Index Test in Predicting the Uniaxial Compressive Strength for Rocks Using Regression Analysis and Machine Learning - Mining, Metallurgy & Exploration (2023). Society for Mining, Metallurgy & Exploration, 2023.

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