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Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network
Abstract
In this study, an artificial neural networks study was carried out to predict the compressive strength of ground granulated blast furnace slag concrete. A data set of a laboratory work, in which a total of 45 concretes were produced, was utilized in the ANNs study. The concrete mixture parameters were three different water-cement ratios (0.3, 0.4, and 0.5), three different cement dosages (350, 400, and 450kg/m^3) and four partial slag replacement ratios (20%, 40%, 60%, and 80%). Compressive strengths of moist cured specimens (22+/-2^oC) were measured at 3, 7, 28, 90, and 360 days. ANN model is constructed, trained and tested using these data. The data used in the ANN model are arranged in a format of six input parameters that cover the cement, ground granulated blast furnace slag, water, hyperplasticizer, aggregate and age of samples and, an output parameter which is compressive strength of concrete. The results showed that ANN can be an alternative approach for the predicting the compressive strength of ground granulated blast furnace slag concrete using concrete ingredients as input parameters.
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Cited by
Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., & Mendis, P. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104440 · Cited by 1,007
“Few studies used ANN to predict the strength of concrete with nanomaterials [41], granulated blast furnace slag [42], fly ash [43], and other mineral admixtures [44].”
BackgroundGolafshani, E. M., Behnood, A., & Arashpour, M. (2020) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2019.117266 · Cited by 478
Predicting compressive strength of lightweight foamed concrete using extreme learning machine model
Yaseen, Z. M., Deo, R. C., Hilal, A., Abd, A. M., Bueno, L. C., Salcedo-Sanz, S., & Nehdi, M. L. (2018) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2017.09.004 · Cited by 434
Mohtasham Moein, M., Saradar, A., Rahmati, K., Ghasemzadeh Mousavinejad, S. H., Bristow, J., Aramali, V., & Karakouzian, M. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.105444 · Cited by 391
Dantas, A. T. A., Batista Leite, M., & de Jesus Nagahama, K. (2013) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2012.09.026 · Cited by 314
Metadata
- Authors
- Bilim, C., Atiş, C. D., Tanyildizi, H., & Karahan, O.
- Venue
- Advances in Engineering Software
- Publisher
- Elsevier BV
- Published
- 2009-05
- Volume
- 40
- Issue
- 5
- Pages
- 334-340
- ISSN
- 0965-9978 (ISSN-L)
- OpenAlex
- W2054557393
- Cited by
- 335
- References
- 0
- FWCI
- 17.16
- Type
- journal article
- Language
- en
- Open access
- closed
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- Bilim, C., Atiş, C. D., Tanyildizi, H., & Karahan, O. (2009). Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network. Advances in Engineering Software. https://doi.org/10.1016/j.advengsoft.2008.05.005
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