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)
- 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: (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: 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.