Smart Structures and Systems

  • 제22권4호
  • /
  • Pages.413-424
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  • 2018
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors

  • Chahnasir, E. Sadeghipour (Department of Civil Engineering, Qeshm International Branch, Islamic Azad University) ;
  • Zandi, Y. (Department of Civil Engineering, Tabriz Branch, Islamic Azad University) ;
  • Shariati, M. (Faculty of Civil Engineering, University of Tabriz) ;
  • Dehghani, E. (Department of Civil Engineering, University of Qom) ;
  • Toghroli, A. (Department of Civil Engineering, Faculty of engineering, University of Malaya) ;
  • Mohamad, E. Tonnizam (Centre of Tropical Geoengineering (GEOTROPIK), Faculty Civil Engineering, Universiti Teknologi Malaysia) ;
  • Shariati, A. (Department of Civil Engineering, South Tehran Branch, Islamic Azad University) ;
  • Safa, M. (Department of Civil Engineering, Faculty of engineering, University of Malaya) ;
  • Wakil, K. (Information Technology Department, Technical College of Informatics, Sulaimani Polytechnic University) ;
  • Khorami, M. (Facultad de Arquitectura y Urbanismo, Universidad Tecnologica Equinoccial, Calle Rumipamba s/n y Bourgeois)
  • 투고 : 2018.01.10
  • 심사 : 2018.09.18
  • 발행 : 2018.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

The factors affecting the shear strength of the angle shear connectors in the steel-concrete composite beams can play an important role to estimate the efficacy of a composite beam. Therefore, the current study has aimed to verify the output of shear capacity of angle shear connector according to the input provided by Support Vector Machine (SVM) coupled with Firefly Algorithm (FFA). SVM parameters have been optimized through the use of FFA, while genetic programming (GP) and artificial neural networks (ANN) have been applied to estimate and predict the SVM-FFA models' results. Following these results, GP and ANN have been applied to develop the prediction accuracy and generalization capability of SVM-FFA. Therefore, SVM-FFA could be performed as a novel model with predictive strategy in the shear capacity estimation of angle shear connectors. According to the results, the Firefly algorithm has produced a generalized performance and be learnt faster than the conventional learning algorithms.

키워드

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