JRMGE / Vol 16 / Issue 11
Comprehensive analysis of multiple machine learning techniques for rock slope failure prediction
Arsalan Mahmoodzadeh, Abed Alanazi, Adil Hussein Mohammed, Hawkar Hashim Ibrahim, Abdullah Alqahtani, Shtwai Alsubai, Ahmed Babeker Elhag
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a Department of Civil Engineering, University of Halabja, Halabja, Kurdistan Region, 46018, Iraq
b Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj, 11942,
Saudi Arabia
c Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil, Kurdistan Region, Iraq
d Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil, 44002, Erbil, Kurdistan Region, Iraq
e Software Engineering Department, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj, 11942, Saudi
Arabia
f Department of Civil Engineering, College of Engineering, King Khalid University, Abha, 61413, Saudi Arabia
2024, 16(11): 4386-4398. doi:10.1016/j.jrmge.2023.08.023
Received: 2023-05-24 / Revised: 2023-07-28 / Accepted: 2023-08-14 / Available online: 2023-12-23
2024, 16(11): 4386-4398.
doi:10.1016/j.jrmge.2023.08.023
Received: 2023-05-24
Revised: 2023-07-28
Accepted: 2023-08-14
Available online: 2023-12-23
In this study, twelve machine learning (ML) techniques are used to accurately estimate the safety factor of rock slopes (SFRS). The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran, evenly distributed between the training (80%) and testing (20%) datasets. The models are evaluated for accuracy using Janbu's limit equilibrium method (LEM) and commercial tool GeoStudio methods. Statistical assessment metrics show that the random forest model is the most accurate in estimating the SFRS (MSE = 0.0182, R2 = 0.8319) and shows high agreement with the results from the LEM method. The results from the long-short-term memory (LSTM) model are the least accurate (MSE = 0.037, R2 = 0.6618) of all the models tested. However, only the null space support vector regression (NuSVR) model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters constant. It is suggested that this model would be the best one to use to calculate the SFRS. A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering difficulties. In this study, we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.
Keywords: Rock slope stability, Open-pit mines, Machine learning (ML), Limit equilibrium method (LEM)
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Arsalan Mahmoodzadeh, Abed Alanazi, Adil Hussein Mohammed, Hawkar Hashim Ibrahim, Abdullah Alqahtani, Shtwai Alsubai, Ahmed Babeker Elhag, 2024. Comprehensive analysis of multiple machine learning techniques for rock slope failure prediction. J. Rock Mech. Geotech. Eng. 16 (11), 4386-4398.
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Author(s) Information
Arsalan Mahmoodzadeh
✉️ arsalan.mahmoodzadeh@uoh.edu.iq
Arsalan Mahmoodzadeh is a doctoral candidate specializing in rock mechanics within the Rock Mechanics Division at the School of Engineering, Tarbiat Modares University, Iran. Additionally, he holds a membership in the international research group at the University of Halabja, Iraq. Arsalan received his BSc degree from the University of Kordestan, Iran, and pursued his MSc degree at Shahrood University of Technology, Iran. His primary research interests revolve around the applications of machine learning and artificial intelligence in the domains of tunneling, rock mechanics, geotechnical engineering, and fracture mechanics. Arsalan has published over 60 research papers, showcasing his extensive contributions to these fields.