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Can I read Estimation of Intact Rock Uniaxial Compressive Strength Using Advanced Machine Learning on EtoBox?
Estimation of Intact Rock Uniaxial Compressive Strength Using Advanced Machine Learning by Jitendra Khatti; Kamaldeep Singh Grover is a Engineering article available to read on EtoBox.
What is Estimation of Intact Rock Uniaxial Compressive Strength Using Advanced Machine Learning about?
The present investigation introduces an optimal computational model by comparing gene expression programming (GEP), least square support vector machine (LSSVM), and extreme learning machine (ELM) models in predicting the uniaxial compressive strength of intact rocks (RUCS). This research employs linear, polynomial, and radial basis function (RBF) kernel-based LSSVM models and compares them. Furthermore, this investigation reveals the effect of chromosomes on the performance of GEP models. One hundred four and 27 results of RUCS have trained and tested RUCS models. In addition, the multicollinearity has been computed for an overall database to analyze its effect on the model's performance and accuracy. This research uses the area and mass of rock specimens, along with Young's modulus, for the first time in predicting the rock UCS. The performance metric comparison reveals that model ELM (mentioned by RUCS7) has predicted rock UCS with a correlation coefficient of 0.9642, root mean square error of 0.0479 MPa, performance index of 1.8067, variance accounted for of 92.49, and agreement index of 0.8681, comparatively higher than LSSVM and GEP models and close to ideal values. The perfor
Who reads Estimation of Intact Rock Uniaxial Compressive Strength Using Advanced Machine Learning?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Jitendra Khatti; Kamaldeep Singh Grover
- Publisher
- Springer Science and Business Media LLC
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)