Practical Machine Learning Application for Predicting Axial Capacity of Composite Concrete-Filled Steel Tube Columns Considering Effect of Cross-Sectional Shapes-Reference-Cited by-同舟云学术

Practical Machine Learning Application for Predicting Axial Capacity of Composite Concrete-Filled Steel Tube Columns Considering Effect of Cross-Sectional Shapes

Published:2022-11-19 Issue:1 Volume:23 Page:263-278
ISSN:1598-2351
Container-title:International Journal of Steel Structures
language:en
Short-container-title:Int J Steel Struct

Author:

Duong T. H.,Le T.-T.^ORCID,Le M. V.

Publisher

Springer Science and Business Media LLC

Subject

Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s13296-022-00693-0.pdf

Reference73 articles.

1. Ahmadi, M., Naderpour, H., & Kheyroddin, A. (2017). ANN model for predicting the compressive strength of circular steel-confined concrete. International Journal of Civil Engineering, 15(2), 213–221. https://doi.org/10.1007/s40999-016-0096-0

2. Al-Khaleefi, A. M., Terro, M. J., Alex, A. P., & Wang, Y. (2002). Prediction of fire resistance of concrete filled tubular steel columns using neural networks. Fire Safety Journal, 37(4), 339–352. https://doi.org/10.1016/S0379-7112(01)00065-0

3. Asteris, P. G., Lemonis, M. E., Le, T.-T., & Tsavdaridis, K. D. (2021). Evaluation of the ultimate eccentric load of rectangular CFSTs using advanced neural network modeling. Engineering Structures, 248, 113297. https://doi.org/10.1016/j.engstruct.2021.113297

4. Basak, D., Pal, S., & Patranabis, D. C. (2007). Support vector regression. Neural Information Processing-Letters and Reviews, 11(10), 203–224.

5. Bilgehan, M. (2011). Comparison of ANFIS and NN models—With a study in critical buckling load estimation. Applied Soft Computing, 11(4), 3779–3791. https://doi.org/10.1016/j.asoc.2011.02.011

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