Comprehensive analysis of multiple machine learning techniques for rock slope failure prediction  

作　　者：Arsalan Mahmoodzadeh Abed Alanazi Adil Hussein Mohammed Hawkar Hashim Ibrahim Abdullah Alqahtani Shtwai Alsubai Ahmed Babeker Elhag 

机构地区：[1]Department of Civil Engineering,University of Halabja,Halabja,Kurdistan Region,46018,Iraq [2]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 [3]Department of Communication and Computer Engineering,Faculty of Engineering,Cihan University-Erbil,Kurdistan Region,Iraq [4]Department of Civil Engineering,College of Engineering,Salahaddin University-Erbil,44002,Erbil,Kurdistan Region,Iraq [5]Software Engineering Department,College of Computer Engineering and Sciences,Prince Sattam bin Abdulaziz University,P.O.Box 151,Al-Kharj,11942,Saudi Arabia [6]Department of Civil Engineering,College of Engineering,King Khalid University,Abha,61413,Saudi Arabia

出　　处：《Journal of Rock Mechanics and Geotechnical Engineering》2024年第11期4386-4398,共13页岩石力学与岩土工程学报（英文）

基　　金：supported via funding from Prince Satam bin Abdulaziz University project number (PSAU/2024/R/1445);The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large Group Research Project (Grant No.RGP.2/357/44).

摘　　要：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.

关 键 词：Rock slope stability Open-pit mines Machine learning(ML) Limit equilibrium method(LEM) 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TU43[自动化与计算机技术—控制科学与工程]