中文版 | English
Title

Optimized data-driven machine learning models for axial strength prediction of rectangular CFST columns

Author
Corresponding AuthorChao Hou
Publication Years
2022-12-02
DOI
Source Title
ISSN
2352-0124
Volume47Pages:760-780
Abstract

The nonlinear material interaction in concrete-filled steel tube (CFST) significantly contributes to its excellent axial compression behaviour, which in the meantime results in highly complex relationships between various specimen parameters and the column strength. Machine learning (ML) technique, being excellent in capturing complicated data mapping, is therefore applied in this study to predict the axial compression strength of rectangular CFST columns. A comprehensive test database containing 1,641 rectangular CFST samples is established. The key input parameters for ML models are identified through both correlation analysis and mechanical principles, highlighting the effects of sectional configuration and column slenderness. Strength prediction models are established applying five mainstream ML methods including the back-propagation neural network, the radial basis function neural network, the adaptive neuro-fuzzy inference system, the Gaussian process regression and the M5 ' model tree. The prediction reasonableness and stability of established models are comprehensively evaluated and compared. Outcomes reveal that the established ML models exhibit higher prediction accuracies and wider applicable ranges than current design standards, whilst the prediction stability of ML models is highly affected by the quantity of the available data set. Finally, ML models are optimized by dividing the training database as per mechanical principles, and a prediction-error-ratio amending method based on ML algorithm is also proposed to further improve the model accuracy.

Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
First ; Corresponding
Funding Project
Shenzhen Science and Technology Program[RCYX20210706092044076] ; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)[K19313901]
WOS Research Area
Engineering
WOS Subject
Engineering, Civil
WOS Accession No
WOS:000899504100001
Publisher
Data Source
人工提交
Citation statistics
Cited Times [WOS]:4
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/416100
DepartmentDepartment of Ocean Science and Engineering
Affiliation
1.Department of Ocean Science and Engineering, Southern University of Science and Technology
2.Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511485, PR China
First Author AffilicationDepartment of Ocean Science and Engineering
Corresponding Author AffilicationDepartment of Ocean Science and Engineering
First Author's First AffilicationDepartment of Ocean Science and Engineering
Recommended Citation
GB/T 7714
Xiao-GuangZhou,Chao Hou,Wei-QiangFeng. Optimized data-driven machine learning models for axial strength prediction of rectangular CFST columns[J]. Structures,2022,47:760-780.
APA
Xiao-GuangZhou,Chao Hou,&Wei-QiangFeng.(2022).Optimized data-driven machine learning models for axial strength prediction of rectangular CFST columns.Structures,47,760-780.
MLA
Xiao-GuangZhou,et al."Optimized data-driven machine learning models for axial strength prediction of rectangular CFST columns".Structures 47(2022):760-780.
Files in This Item:
There are no files associated with this item.
Related Services
Fulltext link
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Export to Excel
Export to Csv
Altmetrics Score
Google Scholar
Similar articles in Google Scholar
[Xiao-GuangZhou]'s Articles
[Chao Hou]'s Articles
[Wei-QiangFeng]'s Articles
Baidu Scholar
Similar articles in Baidu Scholar
[Xiao-GuangZhou]'s Articles
[Chao Hou]'s Articles
[Wei-QiangFeng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xiao-GuangZhou]'s Articles
[Chao Hou]'s Articles
[Wei-QiangFeng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.