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

Naseer Muhammad Khan, Kewang Cao (), Muhammad Zaka Emad (), Sajjad Hussain, Hafeezur Rehman, Kausar Sultan Shah, Faheem Ur Rehman and Aamir Muhammad
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Naseer Muhammad Khan: School of Art, Anhui University of Finance & Economics, Bengbu 233030, China
Kewang Cao: School of Art, Anhui University of Finance & Economics, Bengbu 233030, 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
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

Mathematics, 2022, vol. 10, issue 16, 1-22

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.

Keywords: predictive models; damage factor; thermal treatment; computer simulations; ANNs; ANFIS (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 (5)

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