首页|基于PSO-RF混合算法的盾构参数预测研究

基于PSO-RF混合算法的盾构参数预测研究

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复合地层是盾构隧道施工过程中常见的地层类型,但关于复合地层盾构掘进参数的研究相对较少.为研究不同盾构参数间的相关性,依托长沙轨道交通6号线湖白区间隧道工程,利用随机森林算法(random forest,RF)构建了盾构掘进参数预测模型,结合数理统计和基于随机森林算法(RF)的PSO-RF混合算法模型对盾构掘进参数进行预测,并对预测结果进行误差验证.研究结果表明:将盾构隧道区间的地层物理参数与盾构掘进参数分别作为输入参数和输出参数,导入PSO-RF混合算法模型中进行训练学习,结果显示5种盾构参数预测值与实际值之间的平均绝对百分比误差大多在20%以内,即预测结果达到了泛化能力与预测能力的验证要求.
Research on shield parameter prediction based on the PSO-RF hybrid algorithm
Composite strata is a common type of strata during the process of shield tunnelling.However,there is relatively little research on shield tunneling parameters in composite strata.In order to study the correlation between different shield parameters,in this paper,based on the Changsha Rail Transit Line 6 Hubai interval tunnel project,the shield tunnelling parameter prediction model was constructed using the random forest(RF)algorithm.The shield tunnelling parameters was predicted using the PSO-RF hybrid algorithm model combining the mathematical statistics and the RF.And the prediction results were verified for errors.The results of the study show that the stratigraphic parameters of the shield tunnel intervals are combined with the shield tunneling parameters.Two types of parameters are used as input and output parameters,which are imported into the PSO-RF hybrid algorithm model for training and learning,5 predicted shield tunneling parameters are obtained.The mean absolute percentage error between the predicted and actual values of those parameters is generally less than 20%,which indicate that the prediction results meet the requirements of generalisation and prediction ability.

shield tunneltunneling parameterrandom forestparticle swarm optimization

黄戡、张文杰、李宇健

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长沙理工大学 土木工程学院,湖南 长沙 410114

盾构隧道 掘进参数 随机森林 粒子群算法

国家自然科学基金湖南省自然科学基金湖南省教育厅科学研究重点项目长沙理工大学"双一流"科学研究国际合作拓展项目长沙理工大学土木工程优势特色重点学科创新性项目

520780602020JJ460618A1272018IC1918ZDXK05

2024

交通科学与工程
长沙理工大学

交通科学与工程

影响因子:0.444
ISSN:1674-599X
年,卷(期):2024.40(3)