首页|Prediction of high-embankment settlement combining joint denoising technique and enhanced GWO-v-SVR method

Prediction of high-embankment settlement combining joint denoising technique and enhanced GWO-v-SVR method

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Reliable long-term settlement prediction of a high embankment relates to mountain infrastructure safety.This study developed a novel hybrid model(NHM)that combines a joint denoising technique with an enhanced gray wolf optimizer(EGWO)-v-support vector regression(v-SVR)method.High-embankment field measurements were preprocessed using the joint denoising technique,which in-cludes complete ensemble empirical mode decomposition,singular value decomposition,and wavelet packet transform.Furthermore,high-embankment settlements were predicted using the EGWO-v-SVR method.In this method,the standard gray wolf optimizer(GWO)was improved to obtain the EGWO to better tune the v-SVR model hyperparameters.The proposed NHM was then tested in two case studies.Finally,the influences of the data division ratio and kernel function on the EGWO-v-SVR forecasting performance and prediction efficiency were investigated.The results indicate that the NHM suppresses noise and restores details in high-embankment field measurements.Simultaneously,the NHM out-performs other alternative prediction methods in prediction accuracy and robustness.This demonstrates that the proposed NHM is effective in predicting high-embankment settlements with noisy field mea-surements.Moreover,the appropriate data division ratio and kernel function for EGWO-v-SVR are 7:3 and radial basis function,respectively.

High embankmentSettlement predictionJoint denoising techniqueEnhanced gray wolf optimizerSupport vector regression

Qi Zhang、Qian Su、Zongyu Zhang、Zhixing Deng、De Chen

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School of Civil Engineering,Southwest Jiaotong University,Chengdu,China

MOE Key Laboratory of High Speed Railway Engineering,Southwest Jiaotong University,Chengdu,China

National Natural Science Foundation of ChinaNatural Science Foundation Project of Sichuan Province,ChinaScience and Technology Project of Inner Mongolia Transportation Department,China

518084622023NSFSC0346NJ-2022-14

2024

10.1016/j.jrmge.2023.06.018

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(1)
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