Smart Structures and Systems

  • 제29권3호
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  • Pages.433-444
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  • 2022
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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

Prediction of long-term compressive strength of concrete with admixtures using hybrid swarm-based algorithms

  • Huang, Lihua (School of Management Engineering, Zhejiang Guangsha Vocational and Technical University of Construction) ;
  • Jiang, Wei (School of Intelligent Manufacturing, Zhejiang Guangsha Vocational and Technical University of Construction) ;
  • Wang, Yuling (School of Management Engineering, Zhejiang Guangsha Vocational and Technical University of Construction) ;
  • Zhu, Yirong (Glodon Company Limited) ;
  • Afzal, Mansour (Islamic Azad University)
  • 투고 : 2020.11.19
  • 심사 : 2021.11.24
  • 발행 : 2022.03.25
⟨ 이전 논문 다음 논문 ⟩

초록

Concrete is a most utilized material in the construction industry that have main components. The strength of concrete can be improved by adding some admixtures. Evaluating the impact of fly ash (FA) and silica fume (SF) on the long-term compressive strength (CS) of concrete provokes to find the significant parameters in predicting the CS, which could be useful in the practical works and would be extensible in the future analysis. In this study, to evaluate the effective parameters in predicting the CS of concrete containing admixtures in the long-term and present a fitted equation, the multivariate adaptive regression splines (MARS) method has been used, which could find a relationship between independent and dependent variables. Next, for optimizing the output equation, biogeography-based optimization (BBO), particle swarm optimization (PSO), and hybrid PSOBBO methods have been utilized to find the most optimal conclusions. It could be concluded that for CS predictions in the long-term, all proposed models have the coefficient of determination (R2) larger than 0.9243. Furthermore, MARS-PSOBBO could be offered as the best model to predict CS between three hybrid algorithms accurately.

키워드

참고문헌

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