Machine Learning-Finite Element Mesh Optimization-Based Modeling and Prediction of Excavation-Induced Shield Tunnel Ground Settlement

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成果类型：

期刊论文

作者：

Hu, Da;Hu, Yongjia;Hu, Rong;Tan, Ze;Ni, Pengpeng*;...

通讯作者：

Ni, Pengpeng;Chen, Y

作者机构：

[Xiang, Xuejuan; Tan, Ze; Liu, Jing; Hu, Rong; Hu, Da; Hu, Yongjia; Li, Yongsuo] Hunan City Univ, Hunan Engn Res Ctr Struct Safety & Disaster Preven, Yiyang 413000, Peoples R China.

[Hu, Da; Li, Yongsuo] Hunan City Univ, Sch Civil Engn, Yiyang 413000, Peoples R China.

[Ni, Pengpeng; Ni, PP; Chen, Yu] Sun Yat Sen Univ, Sch Civil Engn, Guangzhou 510275, Peoples R China.

[Ni, Pengpeng; Ni, PP; Chen, Yu] State Key Lab Tunnel Engn, Guangzhou 510275, Peoples R China.

[Ni, Pengpeng; Ni, PP; Chen, Yu] Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China.

通讯机构：

[Ni, PP; Chen, Y ] S

Sun Yat Sen Univ, Sch Civil Engn, Guangzhou 510275, Peoples R China.

State Key Lab Tunnel Engn, Guangzhou 510275, Peoples R China.

Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China.

语种：

英文

关键词：

Settlement prediction;finite element simulation;intelligent optimization algorithm;mesh optimization;finite element-machine learning coupling model

期刊：

INTERNATIONAL JOURNAL OF COMPUTATIONAL METHODS

ISSN：

0219-8762

年：

2024

DOI：

10.1142/S021987622450066X

基金类别：

National Natural Science Foundation of China [52078506, 52174101]; Guangdong Basic and Applied Basic Research Foundation [2023A1515012159, 2023A1515011634]; Natural Science Foundation of Hunan Province [2023JJ30110]; Key Scientific Research Projects of Hunan Provincial Department of Education [23A0568]; Open Research Foundation of Hunan Provincial Key Laboratory of Key Technology on Hydropower Development [PKLHD202005]

机构署名：

本校为第一机构

院系归属：

土木工程学院

摘要：

Ground settlement prediction for shield construction is highly important and challenging. This study introduces a machine learning algorithm combined with finite element numerical simulation, i.e., machine learning-finite element mesh optimization. For surface subsidence prediction, 16 combination models of ANN, KNN, RF and SVR were optimized by PSO, GA, BT and BO, involving raw data preprocessing, principal component analysis, hyperparameter selection and prediction accuracy evaluation. A subway shield tunneling project was analyzed, in which the meshes of finite element numerical models were...

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Machine Learning-Finite Element Mesh Optimization-Based Modeling and Prediction of Excavation-Induced Shield Tunnel Ground Settlement

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