TY - JOUR
T1 - Machine learning (ML) based models for predicting the ultimate strength of rectangular concrete-filled steel tube (CFST) columns under eccentric loading
AU - Wang, Chen
AU - Chan, Tak Ming
N1 - Funding Information:
The research work presented in this paper was supported by the Chinese National Engineering Research Centre for Steel Construction (Hong Kong Branch) and the Seed Funding from the Department of Civil and Environmental Engineering at The Hong Kong Polytechnic University.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2023/2/1
Y1 - 2023/2/1
N2 - Concrete-filled steel tubes (CFSTs) are popularly used in structural applications. The accurate prediction of their ultimate strength is a key for the safety of the structure. Extensive studies have been conducted on the strength prediction of CFSTs under concentric loading. However, in real situation CFSTs are usually subjected to eccentric loading. The combined compression and bending will result in more complex failure mechanisms at the ultimate strength. The accuracy of methods in design codes is usually limited due to their simplicity. In this study, three machine learning (ML) methods, namely, Support Vector Regression (SVR), Random Forest Regression (RFR), and Neural Networks (NN), are adopted to develop models to predict the ultimate strength of CFSTs under eccentric loading. A database consisting of information of 403 experimental tests from literature is created and statistically analyzed. The database was then split to a training set which was used to optimize and train the ML models, and a test set which was used to evaluate performance of trained ML models. Compared with the methods in two typical design codes, the ML models achieved notable improvement in prediction accuracy. The parametric study revealed that the trained ML models could generally capture the effect of each primary input feature, which was verified by the relevant experimental test results.
AB - Concrete-filled steel tubes (CFSTs) are popularly used in structural applications. The accurate prediction of their ultimate strength is a key for the safety of the structure. Extensive studies have been conducted on the strength prediction of CFSTs under concentric loading. However, in real situation CFSTs are usually subjected to eccentric loading. The combined compression and bending will result in more complex failure mechanisms at the ultimate strength. The accuracy of methods in design codes is usually limited due to their simplicity. In this study, three machine learning (ML) methods, namely, Support Vector Regression (SVR), Random Forest Regression (RFR), and Neural Networks (NN), are adopted to develop models to predict the ultimate strength of CFSTs under eccentric loading. A database consisting of information of 403 experimental tests from literature is created and statistically analyzed. The database was then split to a training set which was used to optimize and train the ML models, and a test set which was used to evaluate performance of trained ML models. Compared with the methods in two typical design codes, the ML models achieved notable improvement in prediction accuracy. The parametric study revealed that the trained ML models could generally capture the effect of each primary input feature, which was verified by the relevant experimental test results.
KW - concrete-filled steel tube (CFST)
KW - Eccentric loading
KW - Machine learning
KW - Neural network
KW - Random forest
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85143821137&partnerID=8YFLogxK
U2 - 10.1016/j.engstruct.2022.115392
DO - 10.1016/j.engstruct.2022.115392
M3 - Journal article
AN - SCOPUS:85143821137
SN - 0141-0296
VL - 276
JO - Structural Engineering Review
JF - Structural Engineering Review
M1 - 115392
ER -