科技创新与应用2025，Vol.15Issue(21)：30-34,5.DOI:10.19981/j.CN23-1581/G3.2025.21.006
基于粒子群算法优化的灰色预测模型路基沉降预测分析
曲昌晟 1耿敏1
作者信息
- 1. 大连交通大学 交通工程学院,辽宁 大连 116028
- 折叠
摘要
Abstract
This paper focuses on the prediction of subgrade settlement of high-speed railway.In view of the key impact of subgrade settlement on the stability and smoothness of the line,the grey prediction model is selected after comparing various prediction methods.The principle of GM(1,1)model and the optimization process of particle swarm optimization(PSO)are introduced in detail.The 69～339 day settlement observation data of two sections K417+523 and K417+573 in the first work area of ZH section of Jinan West Railway Station of Beijing Shanghai high speed railway are taken as examples to carry out the case analysis.The results show that the prediction effect of PSO-GM(1,1)model is better than that of GM(1,1)model,and the average fitting errors at the two sections are 3.8%and 3.9%respectively.The residual error test and class ratio deviation test show that it has higher accuracy and better stability.This research provides a new idea for grey prediction model to deal with cumulative errors,and proves that PSO-GM(1,1)model has high reliability and application value in predicting subgrade settlement.关键词
高速铁路/路基沉降/GM(1,1)模型/沉降预测/粒子群算法Key words
high-speed railway/subgrade settlement/GM(1,1)model/settlement prediction/particle swarm algorithm分类
交通工程引用本文复制引用
曲昌晟,耿敏..基于粒子群算法优化的灰色预测模型路基沉降预测分析[J].科技创新与应用,2025,15(21):30-34,5.
