Ultimate axial load of rectangular concrete-filled steel tubes using multiple ANN activation functions

Minas E. Lemonis , Angeliki G. Daramara , Alexandra G. Georgiadou , Vassilis G. Siorikis , Konstantinos Daniel Tsavdaridis , Panagiotis G. Asteris

Abstract

In this paper a model for the prediction of the ultimate axial compressive capacity of square and rectangular Concrete Filled Steel Tubes, based on an Artificial Neural Network modeling procedure is presented. The model is trained and tested using an experimental database, compiled for this reason from the literature that amounts to 1193 specimens, including long, thin-walled and high-strength ones. The proposed model was selected as the optimum from a plethora of alternatives, employing different activation functions in the context of Artificial Neural Network technique. The performance of the developed model was compared against existing methodologies from design codes and from proposals in the literature, employing several performance indices. It was found that the proposed model achieves remarkably improved predictions of the ultimate axial load.

Key Words

artificial neural network; CFST column; soft computing; ultimate axial load

Address

Minas E. Lemonis — Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece
Angeliki G. Daramara — Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece
Alexandra G. Georgiadou — Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece
Vassilis G. Siorikis — Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece
Konstantinos Daniel Tsavdaridis — School of Civil Engineering, Faculty of Engineering and Physical Sciences, University of Leeds, Woodhouse Lane, West Yorkshire, Leeds LS2 9JT, U.K.
Panagiotis G. Asteris — Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece

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