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Smart Structures and Systems
  Volume 29, Number 3, March 2022 , pages 433-444

Prediction of long-term compressive strength of concrete with admixtures using hybrid swarm-based algorithms
Lihua Huang, Wei Jiang, Yuling Wang, Yirong Zhu and Mansour Afzal

    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 longterm 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.
Key Words
    fly ash; high strength concrete; long-term CS prediction; MARS-BBO; MARS-PSO; MARS-PSOBBO; silica fume
(1) Lihua Huang, Yuling Wang:
School of Management Engineering, Zhejiang Guangsha Vocational and Technical University of Construction, Dong Yang, 322100, China;
(2) Wei Jiang:
School of Intelligent Manufacturing, Zhejiang Guangsha Vocational and Technical University of Construction, Dong Yang, 322100, China;
(3) Yirong Zhu:
Glodon Company Limited, Beijing, 100193, China;
(4) Mansour Afzal:
Islamic Azad University, Ardabil, Iran.

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