Appraisal of Different Artificial Intelligence Techniques for the Prediction of Marble Strength

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 and Muhammad Salman
Additional contact information
Muhammad Saqib Jan: School of Art, Anhui University of Finance & Economics, Bengbu 233030, China
Sajjad Hussain: School of Art, Anhui University of Finance & Economics, Bengbu 233030, China
Rida e Zahra: Department of Mining Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
Muhammad Zaka Emad: Department of Mining Engineering, University of Engineering and Technology, Lahore 39161, Pakistan
Naseer Muhammad Khan: Department of Sustainable Advanced Geomechanical Engineering, Military College of Engineering, National University of Sciences and Technology, Risalpur 23200, Pakistan
Zahid Ur Rehman: Department of Mining Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
Kewang Cao: School of Art, Anhui University of Finance & Economics, Bengbu 233030, China
Saad S. Alarifi: Department of Geology and Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Salim Raza: Department of Mining Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
Saira Sherin: Department of Mining Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
Muhammad Salman: Department of Civil Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan

Sustainability, 2023, vol. 15, issue 11, 1-24

Abstract: Rock strength, specifically the uniaxial compressive strength (UCS), is a critical parameter mostly used in the effective and sustainable design of tunnels and other engineering structures. This parameter is determined using direct and indirect methods. The direct methods involve acquiring an NX core sample and using sophisticated laboratory procedures to determine UCS. However, the direct methods are time-consuming, expensive, and can yield uncertain results due to the presence of any flaws or discontinuities in the core sample. Therefore, most researchers prefer indirect methods for predicting rock strength. In this study, UCS was predicted using seven different artificial intelligence techniques: Artificial Neural Networks (ANNs), XG Boost Algorithm, Random Forest (RF), Support Vector Machine (SVM), Elastic Net (EN), Lasso, and Ridge models. The input variables used for rock strength prediction were moisture content (MC), P-waves, and rebound number (R). Four performance indicators were used to assess the efficacy of the models: coefficient of determination (R 2 ), Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE). The results show that the ANN model had the best performance indicators, with values of 0.9995, 0.2634, 0.0694, and 0.1642 for R 2 , RMSE, MSE, and MAE, respectively. However, the XG Boost algorithm model performance was also excellent and comparable to the ANN model. Therefore, these two models were proposed for predicting UCS effectively. The outcomes of this research provide a theoretical foundation for field professionals in predicting the strength parameters of rock for the effective and sustainable design of engineering structures

Keywords: marble strength; direct and indirect methods; correlations analysis; artificial intelligence techniques; performance indicators (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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