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Engineering with Computers

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30.10.2019 | Original Article

Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples

verfasst von: Lei Sun, Mohammadreza Koopialipoor, Danial Jahed Armaghani, Reza Tarinejad, M. M. Tahir

Erschienen in: Engineering with Computers | Ausgabe 2/2021

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Abstract

The successful use of fly ash (FA) and silica fume (SF) materials has been reported in the design of concrete samples in the literature. Due to the benefits of using these materials, they can be utilized in many industrial applications. However, the proper use of them in the right mixes is one of the important factors with respect to the strength and weight of concrete. Therefore, this paper develops relationships based on meta-heuristic (MH) algorithms (artificial bee colony technique) to evaluate the compressive strength of concrete specimens using laboratory experiments. A database comprising silica fume replacement ratio, fly ash replacement ratio, total cementitious material, water content coarse aggregate, high-rate water-reducing agent, fine aggregate, and age of samples, as model inputs, was used to evaluate and predict the compressive strength of concrete samples. Developed models of the MH technique created relationships between the mentioned parameters. In the new models, the influence of each parameter on the compressive strength was determined. Finally, using the developed model, optimum conditions for compressive strength of concrete samples were presented. This paper demonstrated that the MH algorithms are able to develop relationships that can serve as good substitutes for empirical models.

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Titel: Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples
verfasst von: Lei Sun
Mohammadreza Koopialipoor
Danial Jahed Armaghani
Reza Tarinejad
M. M. Tahir
Publikationsdatum: 30.10.2019
Verlag: Springer London
Erschienen in: Engineering with Computers / Ausgabe 2/2021
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI: https://doi.org/10.1007/s00366-019-00875-1

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