Novel Fuzzy-Based Optimization Approaches for the Prediction of Ultimate Axial Load of Circular Concrete-Filled Steel Tubes
Jinsong Liao,
Panagiotis G. Asteris,
Liborio Cavaleri,
Ahmed Salih Mohammed,
Minas E. Lemonis,
Markos Z. Tsoukalas,
Athanasia D. Skentou,
Chrysanthos Maraveas,
Mohammadreza Koopialipoor,
Danial Jahed Armaghani
Affiliations
Jinsong Liao
School of Construction Management, Chongqing Jianzhu College, Chongqing 400072, China
Panagiotis G. Asteris
Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece
Liborio Cavaleri
Department of Engineering, University of Palermo, 90133 Palermo, Italy
Ahmed Salih Mohammed
Civil Engineering Department, College of Engineering, University of Sulaimani, Sulaymaniyah 46001, Iraq
Minas E. Lemonis
Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece
Markos Z. Tsoukalas
Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece
Athanasia D. Skentou
Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece
Chrysanthos Maraveas
Farm Structures Laboratory, Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece
Mohammadreza Koopialipoor
Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran 15914, Iran
Danial Jahed Armaghani
Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, 454080 Chelyabinsk, Russia
An accurate estimation of the axial compression capacity of the concrete-filled steel tubular (CFST) column is crucial for ensuring the safety of structures containing them and preventing related failures. In this article, two novel hybrid fuzzy systems (FS) were used to create a new framework for estimating the axial compression capacity of circular CCFST columns. In the hybrid models, differential evolution (DE) and firefly algorithm (FFA) techniques are employed in order to obtain the optimal membership functions of the base FS model. To train the models with the new hybrid techniques, i.e., FS-DE and FS-FFA, a substantial library of 410 experimental tests was compiled from openly available literature sources. The new model’s robustness and accuracy was assessed using a variety of statistical criteria both for model development and for model validation. The novel FS-FFA and FS-DE models were able to improve the prediction capacity of the base model by 9.68% and 6.58%, respectively. Furthermore, the proposed models exhibited considerably improved performance compared to existing design code methodologies. These models can be utilized for solving similar problems in structural engineering and concrete technology with an enhanced level of accuracy.
