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ANN-based model for predicting the axial load capacity of the cold-formed steel semi-oval hollow section column

ANN-based model for predicting the axial load capacity of the cold-formed steel semi-oval hollow... The cold-formed Steel Semi-oval Hollow Section (SSOHS) column is a new cross-section column and has been used a lot in construction projects. However, the design standards for steel structures in the world have not covered the cross-section classifications for the SSOHS columns in the design process. Therefore, the Axial Load Capacity (ALC) of the SSOHS column has been different between the design standards and experiments. This paper develops predictive tools (formula and graphical user interface) for calculating the ALC of the SSOHS columns based on an Artificial Neural Network (ANN) model. The ANN model has been developed with 219 datasets. The input parameters of the ANN model include the overall depth (D), the overall width (B), thickness (t) of the sections, and the length of the pin-ended columns (L). Meanwhile, the ALC of the SSOHS column is the output parameter of the ANN model. The predictive formula based on an ANN model is compared with three regression models and two existing formulas. The comparison results reveal that the performance of the ANN model outperform three regression models and two existing formulas through indicators: R-squared, RMSE, and a20-index. The sensitivity analyses of the input parameters to the ALC of the SSOHS column are also performed. Finally, a mathematical formula and graphical user interface program are developed to practically calculate the ALC of the SSOHS column. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asian Journal of Civil Engineering Springer Journals

ANN-based model for predicting the axial load capacity of the cold-formed steel semi-oval hollow section column

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References (37)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
1563-0854
eISSN
2522-011X
DOI
10.1007/s42107-022-00560-7
Publisher site
See Article on Publisher Site

Abstract

The cold-formed Steel Semi-oval Hollow Section (SSOHS) column is a new cross-section column and has been used a lot in construction projects. However, the design standards for steel structures in the world have not covered the cross-section classifications for the SSOHS columns in the design process. Therefore, the Axial Load Capacity (ALC) of the SSOHS column has been different between the design standards and experiments. This paper develops predictive tools (formula and graphical user interface) for calculating the ALC of the SSOHS columns based on an Artificial Neural Network (ANN) model. The ANN model has been developed with 219 datasets. The input parameters of the ANN model include the overall depth (D), the overall width (B), thickness (t) of the sections, and the length of the pin-ended columns (L). Meanwhile, the ALC of the SSOHS column is the output parameter of the ANN model. The predictive formula based on an ANN model is compared with three regression models and two existing formulas. The comparison results reveal that the performance of the ANN model outperform three regression models and two existing formulas through indicators: R-squared, RMSE, and a20-index. The sensitivity analyses of the input parameters to the ALC of the SSOHS column are also performed. Finally, a mathematical formula and graphical user interface program are developed to practically calculate the ALC of the SSOHS column.

Journal

Asian Journal of Civil Engineering – Springer Journals

Published: Jul 1, 2023

Keywords: Semi-oval hollow sections; Pin-ended columns; Cold-formed; ANN model; Predicted formula; Graphical user interface

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