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

Author

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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
    Key Laboratory of Deep Coal Resource Mining (China University of Mining & Technology), Ministry of Education, Xuzhou 221116, China
    Department of Mining Engineering, Balochistan University of Information Technology Engineering and Management Sciences, Quetta 87300, Pakistan)

  • Kewang Cao

    (Key Laboratory of Deep Coal Resource Mining (China University of Mining & Technology), Ministry of Education, Xuzhou 221116, China
    School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu 233030, China)

  • Qiupeng Yuan

    (School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu 233030, China
    State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine, Anhui University of Science and Technology, Huainan 232001, 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
    School of Materials and Mineral Resources Engineering, University Sains Malaysia, Engineering Campus, Nibong Tebal 14300, Penang, Malaysia)

  • 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)

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.

Suggested Citation

  • Naseer Muhammad Khan & Kewang Cao & Qiupeng Yuan & Mohd Hazizan Bin Mohd Hashim & Hafeezur Rehman & Sajjad Hussain & Muhammad Zaka Emad & Barkat Ullah & Kausar Sultan Shah & Sajid Khan, 2022. "Application of Machine Learning and Multivariate Statistics to Predict Uniaxial Compressive Strength and Static Young’s Modulus Using Physical Properties under Different Thermal Conditions," Sustainability, MDPI, vol. 14(16), pages 1-27, August.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:9901-:d:885141
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    References listed on IDEAS

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    1. Liang Chen & Xianbiao Mao & Peng Wu, 2020. "Effect of High Temperature and Inclination Angle on Mechanical Properties and Fracture Behavior of Granite at Low Strain Rate," Sustainability, MDPI, vol. 12(3), pages 1-25, February.
    2. Wei Zhang & Tianyi Wang & Dongsheng Zhang & Jiajia Tang & Peng Xu & Xu Duan, 2020. "A Comprehensive Set of Cooling Measures for the Overall Control and Reduction of High Temperature-Induced Thermal Damage in Oversize Deep Mines: A Case Study," Sustainability, MDPI, vol. 12(6), pages 1-18, March.
    3. Mohamed Elgharib Gomah & Guichen Li & Changlun Sun & Jiahui Xu & Sen Yang & Jinghua Li, 2022. "On the Physical and Mechanical Responses of Egyptian Granodiorite after High-Temperature Treatments," Sustainability, MDPI, vol. 14(8), pages 1-22, April.
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    Cited by:

    1. Naseer Muhammad Khan & Kewang Cao & Muhammad Zaka Emad & Sajjad Hussain & Hafeezur Rehman & Kausar Sultan Shah & Faheem Ur Rehman & Aamir Muhammad, 2022. "Development of Predictive Models for Determination of the Extent of Damage in Granite Caused by Thermal Treatment and Cooling Conditions Using Artificial Intelligence," Mathematics, MDPI, vol. 10(16), pages 1-22, August.
    2. Jinrui Zhang & Chuanqi Li & Tingting Zhang, 2023. "An Assessment of the Mobility of Toxic Elements in Coal Fly Ash Using the Featured BPNN Model," Sustainability, MDPI, vol. 15(23), pages 1-18, November.
    3. Sajjad Hussain & Naseer Muhammad Khan & Muhammad Zaka Emad & Abdul Muntaqim Naji & Kewang Cao & Qiangqiang Gao & Zahid Ur Rehman & Salim Raza & Ruoyu Cui & Muhammad Salman & Saad S. Alarifi, 2022. "An Appropriate Model for the Prediction of Rock Mass Deformation Modulus among Various Artificial Intelligence Models," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
    4. Xiaohua Ding & Mehdi Jamei & Mahdi Hasanipanah & Rini Asnida Abdullah & Binh Nguyen Le, 2023. "Optimized Data-Driven Models for Prediction of Flyrock due to Blasting in Surface Mines," Sustainability, MDPI, vol. 15(10), pages 1-20, May.

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