An Appropriate Model for the Prediction of Rock Mass Deformation Modulus among Various Artificial Intelligence Models

Sajjad Hussain, Naseer Muhammad Khan, Muhammad Zaka Emad (), Abdul Muntaqim Naji, Kewang Cao (), Qiangqiang Gao, Zahid Ur Rehman, Salim Raza, Ruoyu Cui, Muhammad Salman and Saad S. Alarifi
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Sajjad Hussain: School of Art, Anhui University of Finance & Economics, Bengbu 233030, China
Naseer Muhammad Khan: Department of Sustainable Advanced Geomechanical Engineering, Military College of Engineering, National University of Sciences and Technology, Risalpur 23200, Pakistan
Muhammad Zaka Emad: Department of Mining Engineering, University of Engineering and Technology, Lahore 39161, Pakistan
Abdul Muntaqim Naji: Department of Geological 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
Qiangqiang Gao: Key Laboratory of Deep Coal Resource Mining, China University of Mining & Technology, Ministry of Education, Xuzhou 221116, China
Zahid Ur Rehman: Department of Mining Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
Salim Raza: Department of Mining Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
Ruoyu Cui: Key Laboratory of Deep Coal Resource Mining, China University of Mining & Technology, Ministry of Education, Xuzhou 221116, China
Muhammad Salman: Department of Civil Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
Saad S. Alarifi: Department of Geology and Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia

Sustainability, 2022, vol. 14, issue 22, 1-22

Abstract: The rock mass deformation modulus (Em) is an essential input parameter in numerical modeling for assessing the rock mass behavior required for the sustainable design of engineering structures. The in situ methods for determining this parameter are costly and time consuming. Their results may not be reliable due to the presence of various natures of joints and following difficult field testing procedures. Therefore, it is imperative to predict the rock mass deformation modulus using alternate methods. In this research, four different predictive models were developed, i.e., one statistical model (Muti Linear Regression (MLR)) and three Artificial Intelligence models (Artificial Neural Network (ANN), Random Forest Regression (RFR), and K-Neighbor Network (KNN)) by employing Rock Mass Rating (RMR 89 ) and Point load index (I 50 ) as appropriate input variables selected through correlation matrix analysis among eight different variables to propose an appropriate model for the prediction of Em. The efficacy of each predictive model was evaluated by using four different performance indicators: performance coefficient R 2 , Mean Absolute Error (MAE), Mean Squared Error (MSE), and Median Absolute Error (MEAE). The results show that the R 2 , MAE, MSE, and MEAE for the ANN model are 0.999, 0.2343, 0.2873, and 0.0814, respectively, which are better than MLR, KNN, and RFR. Therefore, the ANN model is proposed as the most appropriate model for the prediction of Em. The findings of this research will provide a better understanding and foundation for the professionals working in fields during the prediction of various engineering parameters, especially Em for sustainable engineering design in the rock engineering field.

Keywords: rock mass deformation modulus; correlation matrix; intelligence models; performance indicators (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 (1)

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