Next Article in Journal
Evolutionary Game Analysis of Abandoned-Bike-Sharing Recycling: Impact of Recycling Subsidy Policy
Next Article in Special Issue
Damping of Dry Sand in Resonant Column-Torsional Simple Shear Device
Previous Article in Journal
Managing and Governing Integrated Research Programmes: Lessons from Theory and Practice
Previous Article in Special Issue
Salt Cavern Thermal Damage Evolution Investigation Based on a Hybrid Continuum-Discrete Coupled Modeling
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 11
10.3390/su15118835
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

by
Muhammad Saqib Jan
1,2,†,
Sajjad Hussain
1,2,†,
Rida e Zahra
2,
Muhammad Zaka Emad
3,
Naseer Muhammad Khan
4,*,
Zahid Ur Rehman
2,
Kewang Cao
1,5,*,
Saad S. Alarifi
6,
Salim Raza
2,
Saira Sherin
2 and
Muhammad Salman
7
1
School of Art, Anhui University of Finance & Economics, Bengbu 233030, China
2
Department of Mining Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
3
Department of Mining Engineering, University of Engineering and Technology, Lahore 39161, Pakistan
4
Department of Sustainable Advanced Geomechanical Engineering, Military College of Engineering, National University of Sciences and Technology, Risalpur 23200, Pakistan
5
School of Civil, Environmental and Architectural Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
6
Department of Geology and Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
7
Department of Civil Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2023, 15(11), 8835; https://doi.org/10.3390/su15118835
Submission received: 22 February 2023 / Revised: 17 May 2023 / Accepted: 26 May 2023 / Published: 30 May 2023
(This article belongs to the Special Issue Advances in Rock Mechanics and Geotechnical Engineering)
Browse Figures
Review Reports Versions Notes

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 (R2), 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 R2, 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 marble strength; direct and indirect methods; correlations analysis; artificial intelligence techniques; performance indicators

Share and Cite

MDPI and ACS Style

Jan, M.S.; Hussain, S.; e Zahra, R.; Emad, M.Z.; Khan, N.M.; Rehman, Z.U.; Cao, K.; Alarifi, S.S.; Raza, S.; Sherin, S.; et al. Appraisal of Different Artificial Intelligence Techniques for the Prediction of Marble Strength. Sustainability 2023, 15, 8835. https://doi.org/10.3390/su15118835

AMA Style

Jan MS, Hussain S, e Zahra R, Emad MZ, Khan NM, Rehman ZU, Cao K, Alarifi SS, Raza S, Sherin S, et al. Appraisal of Different Artificial Intelligence Techniques for the Prediction of Marble Strength. Sustainability. 2023; 15(11):8835. https://doi.org/10.3390/su15118835

Chicago/Turabian Style

Jan, Muhammad Saqib, Sajjad Hussain, Rida e Zahra, Muhammad Zaka Emad, Naseer Muhammad Khan, Zahid Ur Rehman, Kewang Cao, Saad S. Alarifi, Salim Raza, Saira Sherin, and et al. 2023. "Appraisal of Different Artificial Intelligence Techniques for the Prediction of Marble Strength" Sustainability 15, no. 11: 8835. https://doi.org/10.3390/su15118835

APA Style

Jan, M. S., Hussain, S., e Zahra, R., Emad, M. Z., Khan, N. M., Rehman, Z. U., Cao, K., Alarifi, S. S., Raza, S., Sherin, S., & Salman, M. (2023). Appraisal of Different Artificial Intelligence Techniques for the Prediction of Marble Strength. Sustainability, 15(11), 8835. https://doi.org/10.3390/su15118835

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop