Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/4684
Title: The use of GA-ANNs in the modelling of compressive strength of cement mortar
Authors: Akkurt, Sedat
Özdemir, Serhan
Tayfur, Gökmen
Akyol, Burak
Keywords: Artificial neural networks
Portland cement
Data sets
Compressive strength
Genetic algorithms
Issue Date: Jul-2003
Publisher: Elsevier Ltd.
Source: Akkurt, S., Özdemir, S., Tayfur, G., and Akyol, B. (2003). The use of GA-ANNs in the modelling of compressive strength of cement mortar. Cement and Concrete Research, 33(7), 973-979. doi:10.1016/S0008-8846(03)00006-1
Abstract: In this paper, results of a project aimed at modelling the compressive strength of cement mortar under standard curing conditions are reported. Plant data were collected for 6 months for the chemical and physical properties of the cement that were used in model construction and testing. The training and testing data were separated from the complete original data set by the use of genetic algorithms (GAs). A GA-artificial neural network (ANN) model based on the training data of the cement strength was created. Testing of the model was also done within low average error levels (2.24%). The model was subjected to sensitivity analysis to predict the response of the system to different values of the factors affecting the strength. The plots obtained after sensitivity analysis indicated that increasing the amount of C3S, SO3 and surface area led to increased strength within the limits of the model. C2S decreased the strength whereas C3A decreased or increased the strength depending on the SO3 level. Because of the limited data range used for training, the prediction results were good only within the same range. The utility of the model is in the potential ability to control processing parameters to yield the desired strength levels and in providing information regarding the most favourable experimental conditions to obtain maximum compressive strength.
URI: http://doi.org/10.1016/S0008-8846(03)00006-1
http://hdl.handle.net/11147/4684
ISSN: 0008-8846
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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