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    • Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques
    Authors: Tien-Thinh Le; Panagiotis G. Asteris; Minas E. Lemonis;  Advisor: -;  Co-Author: - (2021)

    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 development of an artificial neural network (ANN) model. An optimization strategy was conducted to obtain a final set of ANN’s architecture as well as its weight and bias parameters. The performance of the developed ANN was then compared to current codes (AS, EN, AIJ, ACI, AISC, LRFD, and DBJ) and existing empirical equations. The accuracy of the present model was found superior to the results obtained by others when predicting the axial load of rectangular CFST columns. For practical ap...