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Development of Predictive Models for Determination of the Extent of Damage in Granite Caused by Thermal Treatment and Cooling Conditions Using Artificial Intelligence

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  • Naseer Muhammad Khan

    (School of Art, Anhui University of Finance & Economics, Bengbu 233030, China
    Department of Sustainable Advanced Geomechanical Engineering, Military College of Engineering, National University of Sciences and Technology, Risalpur 23200, Pakistan
    Department of Mining Engineering, Balochistan University of Information Technology Engineering and Management Sciences, Quetta 87300, Pakistan)

  • Kewang Cao

    (School of Art, Anhui University of Finance & Economics, Bengbu 233030, China
    State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Muhammad Zaka Emad

    (Department of Mining Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Sajjad Hussain

    (Department of Mining Engineering, University of Engineering & Technology, Peshawar 25000, Pakistan)

  • Hafeezur Rehman

    (Department of Mining Engineering, Balochistan University of Information Technology Engineering and Management Sciences, Quetta 87300, Pakistan
    School of Materials and Minerals Resources Engineering, University Sains Malaysia, Nibong Tebal 14300, Penang, Malaysia)

  • Kausar Sultan Shah

    (School of Materials and Minerals Resources Engineering, University Sains Malaysia, Nibong Tebal 14300, Penang, Malaysia)

  • Faheem Ur Rehman

    (Graduate School of Economics and Management, Ural Federal University, Mira 19, 620002 Ekaterinburg, Russia)

  • Aamir Muhammad

    (Mineral Development Department Government of KP, Peshawar 25000, Pakistan)

Abstract

Thermal treatment followed by subsequent cooling conditions (slow and rapid) can induce damage to the rock surface and internal structure, which may lead to the instability and failure of the rock. The extent of the damage is measured by the damage factor ( D T ), which can be quantified in a laboratory by evaluating the changes in porosity, elastic modulus, ultrasonic velocities, acoustic emission signals, etc. However, the execution process for quantifying the damage factor necessitates laborious procedures and sophisticated equipment, which are time-consuming, costly, and may require technical expertise. Therefore, it is essential to quantify the extent of damage to the rock via alternate computer simulations. In this research, a new predictive model is proposed to quantify the damage factor. Three predictive models for quantifying the damage factors were developed based on multilinear regression (MLR), artificial neural networks (ANNs), and the adoptive neural-fuzzy inference system (ANFIS). The temperature ( T ), porosity ( ρ ), density ( D ), and P-waves were used as input variables in the development of predictive models for the damage factor. The performance of each predictive model was evaluated by the coefficient of determination (R 2 ), the A20 index, the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the variance accounted for (VAF). The comparative analysis of predictive models revealed that ANN models used for predicting the rock damage factor based on porosity in slow conditions give an R 2 of 0.99, A20 index of 0.99, RMSE of 0.01, MAPE of 0.14, and a VAF of 100%, while rapid cooling gives an R 2 of 0.99, A20 index of 0.99, RMSE of 0.02, MAPE of 0.36%, and a VAF of 99.99%. It has been proposed that an ANN-based predictive model is the most efficient model for quantifying the rock damage factor based on porosity compared to other models. The findings of this study will facilitate the rapid quantification of damage factors induced by thermal treatment and cooling conditions for effective and successful engineering project execution in high-temperature rock mechanics environments.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2883-:d:886141
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    References listed on IDEAS

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    1. 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.
    2. Yanjun Zhang & Ling Zhou & Zhongjun Hu & Ziwang Yu & Shuren Hao & Zhihong Lei & Yangyang Xie, 2018. "Prediction of Layered Thermal Conductivity Using Artificial Neural Network in Order to Have Better Design of Ground Source Heat Pump System," Energies, MDPI, vol. 11(7), pages 1-25, July.
    3. Ling Zhang & Jianye Liu & Jizhou Lai & Zhi Xiong, 2014. "Performance Analysis of Adaptive Neuro Fuzzy Inference System Control for MEMS Navigation System," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-7, January.
    4. Yan-Jun Shen & Yu-Liang Zhang & Feng Gao & Geng-She Yang & Xing-Ping Lai, 2018. "Influence of Temperature on the Microstructure Deterioration of Sandstone," Energies, MDPI, vol. 11(7), pages 1-17, July.
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    Cited by:

    1. Linqi Huang & Shaofeng Wang & Xin Cai & Zhengyang Song, 2022. "Mathematical Problems in Rock Mechanics and Rock Engineering," Mathematics, MDPI, vol. 11(1), pages 1-3, December.
    2. Muhammad Saqib Jan & Sajjad Hussain & Rida e Zahra & Muhammad Zaka Emad & Naseer Muhammad Khan & Zahid Ur Rehman & Kewang Cao & Saad S. Alarifi & Salim Raza & Saira Sherin & Muhammad Salman, 2023. "Appraisal of Different Artificial Intelligence Techniques for the Prediction of Marble Strength," Sustainability, MDPI, vol. 15(11), pages 1-24, May.
    3. Lijian Zhou & Lijun Wang & Zhiang Zhao & Yuwei Liu & Xiwu Liu, 2022. "A Seq2Seq Model Improved by Transcendental Learning and Imaged Sequence Samples for Porosity Prediction," Mathematics, MDPI, vol. 11(1), pages 1-23, December.
    4. Mohamed Elgharib Gomah & Guichen Li & Naseer Muhammad Khan & Changlun Sun & Jiahui Xu & Ahmed A. Omar & B. G. Mousa & Marzouk Mohamed Aly Abdelhamid & M. M. Zaki, 2022. "Prediction of Strength Parameters of Thermally Treated Egyptian Granodiorite Using Multivariate Statistics and Machine Learning Techniques," Mathematics, MDPI, vol. 10(23), pages 1-21, November.
    5. 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.

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