Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques

This work aims to develop a novel and practical equation for predicting the axial load of rectangular concrete-filled steel tubular (CFST) columns based on soft computing techniques. More precisely, a dataset containing 880 experimental tests was first collected from the available literature for the...

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Veröffentlicht in:	Engineering with computers 2022-10, Vol.38 (Suppl 4), p.3283-3316
Hauptverfasser:	Le, Tien-Thinh, Asteris, Panagiotis G., Lemonis, Minas E.
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Axial loads CAE) and Design Calculus of Variations and Optimal Control Optimization Classical Mechanics Computer Science Computer-Aided Engineering (CAD Concrete columns Control Empirical equations Graphical user interface Machine learning Math. Applications in Chemistry Mathematical and Computational Engineering Optimization Original Article Soft computing Steel columns Steel tubes Systems Theory User interface
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