Prediction of Axial Compression Capacity of Cold-Formed Steel Oval Hollow Section Columns Using ANN and ANFIS Models

The steel oval hollow section (OHS) provides an aesthetic architecture and a greater local buckling strength. However, the existing design codes do not specify the effective width in calculating the load-bearing capacity of OHS members. This study aims to predict the axial compression capacity (ACC)...

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Bibliographic Details
Published in:International journal of steel structures 2022, 22(1), , pp.1-26
Main Authors: Nguyen, Trong-Ha, Tran, Ngoc-Long, Nguyen, Duy-Duan
Format: Article
Language:English
Subjects:
Artificial neural networks
Axial compression
Bearing capacity
Civil Engineering
Cold working
Cold-formed steel
Engineering
Fuzzy logic
Graphical user interface
Hollow sections
Learning theory
Load bearing elements
Machine learning
Materials Science
Mathematical models
Parameters
Solid Mechanics
Steel columns
토목공학
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Summary:The steel oval hollow section (OHS) provides an aesthetic architecture and a greater local buckling strength. However, the existing design codes do not specify the effective width in calculating the load-bearing capacity of OHS members. This study aims to predict the axial compression capacity (ACC) of cold-formed steel OHS columns using artificial neural network (ANN) and adaptive neural fuzzy inference system (ANFIS) models. A total of 128 data sets collected from the literature were utilized to develop the ANN and ANFIS models. The performance of the two machine learning models was compared with three existing design codes. The results demonstrated that the developed ANN and ANFIS models predicted the ACC of steel OHS columns more accurately compared to the existing formulas. Specifically, the ANN model revealed a superior performance with the highest coefficient of determination and the smallest root means square errors. Moreover, the formulas based on ANN and ANFIS models, which accommodates all input parameters, were proposed to predict the ACC of cold-formed steel OHS columns. The thickness of the cross-section was the most influential parameter on the ACC of the OHS column. By contrast, the column length negatively affected the ACC value of the steel column. Finally, a graphical user interface tool was developed to readily calculate the ACC of the steel OHS columns.
ISSN:1598-2351
2093-6311
DOI:10.1007/s13296-021-00557-z