• ISSN: 2010-0248 (Print)
    • Abbreviated Title: Int. J. Innov.  Manag. Technol.
    • Frequency: Quarterly
    • DOI: 10.18178/IJIMT
    • Editor-in-Chief: Prof. Jin Wang
    • Managing Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: Google Scholar, CNKI, Ulrich's Periodicals Directory,  Crossref, Electronic Journals Library.
    • E-mail: ijimt@ejournal.net
HOME > Archive > 2019 > Volume 10 Number 1 (Feb. 2019) >
IJIMT 2019 Vol.10(1): 33-37 ISSN: 2010-0248
doi: 10.18178/ijimt.2019.10.1.832

Cost Optimization of No-Slump Concrete Using Genetic Algorithm and Particle Swarm Optimization

Hamed Naseri

Abstract— Overall cost optimization of no-slump concrete (NSC) is investigated in this study. In this investigation, some restriction for the amount of compressive strength is considered and overall cost of concrete is minimized by designing mixture proportion. Cement, silica fume, water, fine aggregate, coarse aggregate and filler are the materials that are used in mentioned concrete. In this study, the purpose is to find the cheapest concrete that its compressive strength is 65 MPa. Genetic algorithm (GA) and particle swarm optimization algorithm (PSO) are used to find the best solutions. The results indicate that PSO introduced mixture proportion is 2% cheaper than that of GA. Also, running GA takes two times as much time as running PSO.

Index Terms— Compressive strength, cost, genetic algorithm (GA), optimization, particle swarm optimization (PSO).

Hamed Naseri is with Iran University of Science and Technology (IUST), Iran (e-mail: hamed_naseri1993@yahoo.com).

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Cite: Hamed Naseri, " Cost Optimization of No-Slump Concrete Using Genetic Algorithm and Particle Swarm Optimization," International Journal of Innovation, Management and Technology vol. 10, no. 1, pp. 33-37, 2019.

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