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

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作者信息关键词期刊信息基础信息归属信息摘要

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

卷：

期：

页码：

2450066

DOI：

10.1142/S021987622450066X

基金类别：

National Natural Science Foundation of China#&#&#52078506 Guangdong Basic and Applied Basic Research Foundation#&#&#2023A1515012159 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 National Natural Science Foundation of China#&#&#52174101 Guangdong Basic and Applied Basic Research Foundation#&#&#2023A1515011634

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...

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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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