Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5591
Title: Strength prediction of high-strength concrete by fuzzy logic and artificial neural networks
Authors: Tayfur, Gökmen
Erdem, Tahir Kemal
Kırca, Önder
Tayfur, Gökmen
Erdem, Tahir Kemal
Izmir Institute of Technology. Civil Engineering
Keywords: Artificial intelligence
Cement
Compressive strength
Concrete admixtures
Fuzzy sets
Issue Date: 1-Nov-2014
Publisher: American Society of Civil Engineers (ASCE)
Source: Tayfur, G., Erdem, T.K., and Kırca, Ö. (2014). Strength prediction of high-strength concrete by fuzzy logic and artificial neural networks. Journal of Materials in Civil Engineering, 26(11). doi:10.1061/(ASCE)MT.1943-5533.0000985
Abstract: High-strength concretes (HSC) were prepared with five different binder contents, each of which had several silica fume (SF) ratios (0-15%). The compressive strength was determined at 3, 7, and 28 days, resulting in a total of 60 sets of data. In a fuzzy logic (FL) algorithm, three input variables (SF content, binder content, and age) and the output variable (compressive strength) were fuzzified using triangular membership functions. A total of 24 fuzzy rules were inferred from 60% of the data. Moreover, the FL model was tested against an artificial neural networks (ANNs) model. The results show that FL can successfully be applied to predict the compressive strength of HSC. Three input variables were sufficient to obtain accurate results. The operators used in constructing the FL model were found to be appropriate for compressive strength prediction. The performance of FL was comparable to that of ANN. The extrapolation capability of FL and ANNs were found to be satisfactory.
URI: https://doi.org/10.1061/(ASCE)MT.1943-5533.0000985
http://hdl.handle.net/11147/5591
ISSN: 0899-1561
1943-5533
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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