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Prediction of Uniaxial Compressive Strength of Rock Using Machine Learning
The uniaxial compressive strength of rock is one of the most significant parameters required for analysis of rock mass, its characterization, and design of foundations. Direct determination of the uniaxial compressive strength of rock is time-consuming, expensive, and requires destructive laboratory or field testing. Therefore, indirect methods based on regression analysis are widely used for estimation of the uniaxial compressive strength of rock, which have less accuracy. In this study, machine learning algorithms are used to estimate the uniaxial compressive strength of rock using point load strength, porosity, Schmidt rebound hardness, block punch index, and specific gravity. The performance of each machine learning model is evaluated using statistical parameters, viz., mean absolute error, value account for, and coefficient of determination. It is found that random forest regression is the most suitable model for estimation of uniaxial compressive strength with the minimum mean absolute error of 8.68 MPa and r2-score of 0.94.
Prediction of Uniaxial Compressive Strength of Rock Using Machine Learning
The uniaxial compressive strength of rock is one of the most significant parameters required for analysis of rock mass, its characterization, and design of foundations. Direct determination of the uniaxial compressive strength of rock is time-consuming, expensive, and requires destructive laboratory or field testing. Therefore, indirect methods based on regression analysis are widely used for estimation of the uniaxial compressive strength of rock, which have less accuracy. In this study, machine learning algorithms are used to estimate the uniaxial compressive strength of rock using point load strength, porosity, Schmidt rebound hardness, block punch index, and specific gravity. The performance of each machine learning model is evaluated using statistical parameters, viz., mean absolute error, value account for, and coefficient of determination. It is found that random forest regression is the most suitable model for estimation of uniaxial compressive strength with the minimum mean absolute error of 8.68 MPa and r2-score of 0.94.
Prediction of Uniaxial Compressive Strength of Rock Using Machine Learning
J. Inst. Eng. India Ser. A
Dadhich, Sharad (author) / Sharma, Jitendra Kumar (author) / Madhira, Madhav (author)
Journal of The Institution of Engineers (India): Series A ; 103 ; 1209-1224
2022-12-01
16 pages
Article (Journal)
Electronic Resource
English
Estimation of Intact Rock Uniaxial Compressive Strength Using Advanced Machine Learning
| Springer Verlag | 2024
Estimation of Intact Rock Uniaxial Compressive Strength Using Advanced Machine Learning
| Springer Verlag | 2024