Techno Press
You logged in as Techno Press

Steel and Composite Structures
  Volume 44, Number 1, July10 2022 , pages 119-139

An artificial intelligence-based design model for circular CFST stub columns under axial load
Süleyman İpek, Ayşegül Erdoğan and Esra Mete Güneyisi

    This paper aims to use the artificial intelligence approach to develop a new model for predicting the ultimate axial strength of the circular concrete-filled steel tubular (CFST) stub columns. For this, the results of 314 experimentally tested circular CFST stub columns were employed in the generation of the design model. Since the influence of the column diameter, steel tube thickness, concrete compressive strength, steel tube yield strength, and column length on the ultimate axial strengths of columns were investigated in these experimental studies, here, in the development of the design model, these variables were taken into account as input parameters. The model was developed using the backpropagation algorithm named Bayesian Regularization. The accuracy, reliability, and consistency of the developed model were evaluated statistically, and also the design formulae given in the codes (EC4, ACI, AS, AIJ, and AISC) and the previous empirical formulations proposed by other researchers were used for the validation and comparison purposes. Based on this evaluation, it can be expressed that the developed design model has a strong and reliable prediction performance with a considerably high coefficient of determination (R-squared) value of 0.9994 and a low average percent error of 4.61. Besides, the sensitivity of the developed model was also monitored in terms of dimensional properties of columns and mechanical characteristics of materials. As a consequence, it can be stated that for the design of the ultimate axial capacity of the circular CFST stub columns, a novel artificial intelligence-based design model with a good and robust prediction performance was proposed herein.
Key Words
    CFST column; code formula; design model; artificial neural network; stub column
Süleyman İpek:Department of Architecture, Bingol University, 12000, Bingöl, Turkey

Ayşegül Erdoğan:Department of Civil Engineering, Gaziantep University, 27310, Gaziantep, Turkey

Esra Mete Güneyisi:Department of Civil Engineering, Gaziantep University, 27310, Gaziantep, Turkey


Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2024 Techno Press
P.O. Box 33, Yuseong, Daejeon 305-600 Korea, Tel: +82-42-828-7996, Fax : +82-42-828-7997, Email: