首页|Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

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The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications.This study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJ-CSA-MLPnet.First of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal variances.This technique is expected to improve the accuracy of friction angle prediction models.The friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,i.e.FPA-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,i.e.YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJ-CSA-MLPnet.Among these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83％in predicting the friction angle of clay in practical engineering scenarios.This improvement is significant,as it represents an increase from 1.3％to approximately 20％compared to the models that did not utilize the YJ transformation technique.

Natural hazardsSlope stabilityFriction angleClaySoft computing modelsGeotechnical engineering

Libing Yang、Trung Nguyen-Thoi、Trung-Tin Tran

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College of Civil Engineering and Architecture,Hunan Institute of Science and Technology,Yueyang,414006,China

Laboratory for Applied and Industrial Mathematics,Institute for Computational Science and Artificial Intelligence,Van Lang University,Ho Chi Minh City,Viet Nam

Faculty of Mechanical-Electrical and Computer Engineering,School of Technology,Van Lang University,Ho Chi Minh City,Viet Nam

Department of Information Technology,Greenwich Vietnam,FPT University,Da Nang,550000,Viet Nam

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Hunan Provincial Natural Science Foundation of ChinaHunan Provincial Natural Science Foundation of ChinaGeneral Project of Education Department of Hunan Province,China

2021JJ303012023JJ5028121C0495

2024

10.1016/j.jrmge.2024.02.025

岩石力学与岩土工程学报(英文版)
中国科学院武汉岩土力学所中国岩石力学与工程学会武汉大学

岩石力学与岩土工程学报(英文版)

CSTPCD
影响因子：0.404
ISSN：1674-7755
年,卷(期)：2024.16(10)