太原理工大学学报2025，Vol.56Issue(5)：907-915,9.DOI:10.16355/j.tyut.1007-9432.20240599
基于变分模态分解和粒子群优化长短期记忆网络的黄土地区高填方路基沉降预测
Settlement Prediction of High Fill Subgrade in Loess Areas Based on Variational Mode Decomposition and Particle Swarm Optimization of Long Short-Term Memory Networks
摘要
Abstract
[Purposes]This research is conducted to achieve accurate prediction of settlement trends for high fill subgrades in loess areas.[Methods]In this work,a Variational Mode Decomposi-tion(VMD)and Particle Swarm Optimization(PSO)optimized Long Short-Term Memory(LSTM)network prediction model was proposed,referred to as VMD-PSO-LSTM model.This model was designed to learn high-level features of the settlement data for high fill subgrade and predict their de-velopmental trends.To validate the effectiveness of the proposed model,an engineering case study was conducted.[Results]The results clearly indicate that the VMD-PSO-LSTM model performs well in predicting the settlement curves of high fill subgrade.Moreover,its accuracy surpasses that of the Back Propagation Neural Network(BP)model,the standard LSTM model,and the LSTM model optimized solely by PSO(PSO-LSTM),suggestings that the proposed VMD-PSO-LSTM model not only provides enhanced predictive accuracy but also demonstrates increased robustness and wider applicability.关键词
黄土/高填方路基/沉降预测/变分模态分解/长短期记忆网络Key words
loess/high fill subgrade/settlement prediction/variational mode decomposition/long short-term memory network分类
建筑与水利引用本文复制引用
柴少波,岳山丘,王铭一,吕龙龙,范康凯..基于变分模态分解和粒子群优化长短期记忆网络的黄土地区高填方路基沉降预测[J].太原理工大学学报,2025,56(5):907-915,9.基金项目
国家自然科学基金项目(41902277) （41902277）
宁夏教育厅高等学校科学研究项目青年支持项目(NYG2024051) （NYG2024051）
