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Title: Fuzzy logic model for the prediction of cement compressive strength
Authors: Akkurt, Sedat
Tayfur, Gökmen
Can, Sever
Akkurt, Sedat
Tayfur, Gökmen
Can, Sever
Izmir Institute of Technology. Mechanical Engineering
Izmir Institute of Technology. Civil Engineering
Keywords: Artificial neural networks
Compressive strength
Fuzzy logic
Issue Date: Aug-2004
Publisher: Elsevier Ltd.
Source: Akkurt, S., Tayfur, G., and Can, S. (2004). Fuzzy logic model for the prediction of cement compressive strength. Cement and Concrete Research, 34(8), 1429-1433. doi:10.1016/j.cemconres.2004.01.020
Abstract: A fuzzy logic prediction model for the 28-day compressive strength of cement mortar under standard curing conditions was created. Data collected from a cement plant were used in the model construction and testing. The input variables of alkali, Blaine, SO3, and C3S and the output variable of 28-day cement strength were fuzzified by the use of artificial neural networks (ANNs), and triangular membership functions were employed for the fuzzy subsets. The Mamdani fuzzy rules relating the input variables to the output variable were created by the ANN model and were laid out in the If-Then format. Product (prod) inference operator and the centre of gravity (COG; centroid) defuzzification methods were employed. The prediction of 50 sets of the 28-day cement strength data by the developed fuzzy model was quite satisfactory. The average percentage error levels in the fuzzy model were successfully low (2.69%). The model was compared with the ANN model for its error levels and ease of application. The results indicated that through the application of fuzzy logic algorithm, a more user friendly and more explicit model than the ANNs could be produced within successfully low error margins.
ISSN: 0008-8846
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Mechanical Engineering / Makina Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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