Application of Machine Learning and Multivariate Statistics to Predict Uniaxial Compressive Strength and Static Young’s Modulus Using Physical Properties under Different Thermal Conditions

Naseer Muhammad Khan, Kewang Cao, Qiupeng Yuan (), Mohd Hazizan Bin Mohd Hashim, Hafeezur Rehman (), Sajjad Hussain, Muhammad Zaka Emad, Barkat Ullah, Kausar Sultan Shah and Sajid Khan
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Naseer Muhammad Khan: Department of Sustainable Advanced Geomechanical Engineering, Military College of Engineering, National University of Sciences and Technology, Risalpur 23200, Pakistan
Kewang Cao: Key Laboratory of Deep Coal Resource Mining (China University of Mining & Technology), Ministry of Education, Xuzhou 221116, China
Qiupeng Yuan: School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu 233030, China
Mohd Hazizan Bin Mohd Hashim: School of Materials and Mineral Resources Engineering, University Sains Malaysia, Engineering Campus, Nibong Tebal 14300, Penang, Malaysia
Hafeezur Rehman: Department of Mining Engineering, Balochistan University of Information Technology Engineering and Management Sciences, Quetta 87300, Pakistan
Sajjad Hussain: Department of Mining Engineering, University of Engineering & Technology, Peshawar 25000, Pakistan
Muhammad Zaka Emad: Department of Mining Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
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
Sajid Khan: Department of Mining Engineering, University of Engineering & Technology, Peshawar 25000, Pakistan

Sustainability, 2022, vol. 14, issue 16, 1-27

Abstract: Uniaxial compressive strength (UCS) and the static Young’s modulus (E s ) are fundamental parameters for the effective design of engineering structures in a rock mass environment. Determining these two parameters in the laboratory is time-consuming and costly, and the results may be inappropriate if the testing process is not properly executed. Therefore, most researchers prefer alternative methods to estimate these two parameters. This work evaluates the thermal effect on the physical, chemical, and mechanical properties of marble rock, and proposes a prediction model for UCS and E S using multi-linear regression (MLR), artificial neural networks (ANNs), random forest (RF), and k-nearest neighbor. The temperature (T), P-wave velocity (P V ), porosity (η), density (ρ), and dynamic Young’s modulus (E d ) were taken as input variables for the development of predictive models based on MLR, ANN, RF, and KNN. Moreover, the performance of the developed models was evaluated using the coefficient of determination (R 2 ) and mean square error (MSE). The thermal effect results unveiled that, with increasing temperature, the UCS, E S , P V , and density decrease while the porosity increases. Furthermore, ES and UCS prediction models have an R 2 of 0.81 and 0.90 for MLR, respectively, and 0.85 and 0.95 for ANNs, respectively, while KNN and RF have given the R 2 value of 0.94 and 0.97 for both E S and UCS. It is observed from the statistical analysis that P-waves and temperature show a strong correlation under the thermal effect in the prediction model of UCS and E S . Based on predictive performance, the RF model is proposed as the best model for predicting UCS and E S under thermal conditions.

Keywords: thermal effect prediction model; uniaxial compressive strength; static Young’s modulus; artificial neural network; multilinear regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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