Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples
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 th...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
Springer Science and Business Media Deutschland GmbH
2021
|
| Subjects: |
| _version_ | 1796865850785923072 |
|---|---|
| author | Lei, Sun Koopialipoor, Mohammadreza Armaghani, Danial Jahed Tarinejad, Reza Tahir, M. M. |
| author_facet | Lei, Sun Koopialipoor, Mohammadreza Armaghani, Danial Jahed Tarinejad, Reza Tahir, M. M. |
| author_sort | Lei, Sun |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T21:03:22Z |
| format | Article |
| id | utm.eprints-94581 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:03:22Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | utm.eprints-945812022-03-31T15:48:04Z http://eprints.utm.my/94581/ Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples Lei, Sun Koopialipoor, Mohammadreza Armaghani, Danial Jahed Tarinejad, Reza Tahir, M. M. TA Engineering (General). Civil engineering (General) 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. Springer Science and Business Media Deutschland GmbH 2021 Article PeerReviewed Lei, Sun and Koopialipoor, Mohammadreza and Armaghani, Danial Jahed and Tarinejad, Reza and Tahir, M. M. (2021) Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples. Engineering with Computers, 37 (2). pp. 1133-1145. ISSN 0177-0667 http://dx.doi.org/10.1007/s00366-019-00875-1 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Lei, Sun Koopialipoor, Mohammadreza Armaghani, Danial Jahed Tarinejad, Reza Tahir, M. M. Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples |
| title | Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples |
| title_full | Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples |
| title_fullStr | Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples |
| title_full_unstemmed | Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples |
| title_short | Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples |
| title_sort | applying a meta heuristic algorithm to predict and optimize compressive strength of concrete samples |
| topic | TA Engineering (General). Civil engineering (General) |
| work_keys_str_mv | AT leisun applyingametaheuristicalgorithmtopredictandoptimizecompressivestrengthofconcretesamples AT koopialipoormohammadreza applyingametaheuristicalgorithmtopredictandoptimizecompressivestrengthofconcretesamples AT armaghanidanialjahed applyingametaheuristicalgorithmtopredictandoptimizecompressivestrengthofconcretesamples AT tarinejadreza applyingametaheuristicalgorithmtopredictandoptimizecompressivestrengthofconcretesamples AT tahirmm applyingametaheuristicalgorithmtopredictandoptimizecompressivestrengthofconcretesamples |