Predicting Angle of Internal Friction and Cohesion of Rocks Based on Machine Learning Algorithms

Niaz Muhammad Shahani (), Barkat Ullah (), Kausar Sultan Shah, Fawad Ul Hassan, Rashid Ali, Mohamed Abdelghany Elkotb, Mohamed E. Ghoneim and Elsayed M. Tag-Eldin
Additional contact information
Niaz Muhammad Shahani: School of Mines, China University of Mining and Technology, Xuzhou 221116, China
Barkat Ullah: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Kausar Sultan Shah: Department of Mining Engineering, Karakoram International University, Gilgit 15100, Pakistan
Fawad Ul Hassan: School of Mines, China University of Mining and Technology, Xuzhou 221116, China
Rashid Ali: School of Mathematics and Statistics, Central South University, Changsha 410083, China
Mohamed Abdelghany Elkotb: Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia
Mohamed E. Ghoneim: Department of Mathematical Sciences, Faculty of Applied Science, Umm Al-Qura University, Makkah 21955, Saudi Arabia
Elsayed M. Tag-Eldin: Center of Research and Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt

Mathematics, 2022, vol. 10, issue 20, 1-17

Abstract: The safe and sustainable design of rock slopes, open-pit mines, tunnels, foundations, and underground excavations requires appropriate and reliable estimation of rock strength and deformation characteristics. Cohesion (𝑐) and angle of internal friction (𝜑) are the two key parameters widely used to characterize the shear strength of materials. Thus, the prediction of these parameters is essential to evaluate the deformation and stability of any rock formation. In this study, four advanced machine learning (ML)-based intelligent prediction models, namely Lasso regression (LR), ridge regression (RR), decision tree (DT), and support vector machine (SVM), were developed to predict 𝑐 in (MPa) and 𝜑 in (°), with P-wave velocity in (m/s), density in (gm/cc), UCS in (MPa), and tensile strength in (MPa) as input parameters. The actual dataset having 199 data points with no missing data was allocated identically for each model with 70% for training and 30% for testing purposes. To enhance the performance of the developed models, an iterative 5-fold cross-validation method was used. The coefficient of determination (R 2 ), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and a10-index were used as performance metrics to evaluate the optimal prediction model. The results revealed the SVM to be a more efficient model in predicting 𝑐 (R 2 = 0.977) and 𝜑 (R 2 = 0.916) than LR (𝑐: R 2 = 0.928 and 𝜑: R 2 = 0.606), RR (𝑐: R 2 = 0.961 and 𝜑: R 2 = 0.822), and DT (𝑐: R 2 = 0.934 and 𝜑: R 2 = 0.607) on the testing data. Furthermore, to check the level of accuracy of the SVM model, a sensitivity analysis was performed on the testing data. The results showed that UCS and tensile strength were the most influential parameters in predicting 𝑐 and 𝜑. The findings of this study contribute to long-term stability and deformation evaluation of rock masses in surface and subsurface rock excavations.

Keywords: angle of internal friction; cohesion; geotechnical parameters; support vector machine; intelligent prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/20/3875/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/20/3875/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:20:p:3875-:d:946551

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().