Universal Journal of Geoscience Vol. 4(2), pp. 23 - 30
DOI: 10.13189/ujg.2016.040202
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Modelling of Multi Layer Feed forward Neural Networks to Determine the Compressive Strength of Marmara Region Aggregate's Concrete

Okan Özbakır ^*, Erkin Nasuf
Mining Engineering, Mining Faculty, Istanbul Technical University, Turkey

ABSTRACT

We aim to estimate concrete compressive strength by using the physical properties of the aggregates that are the main components of concrete. For this aim, concrete samples were prepared with aggregates having different origins and characteristics obtained from different 10 locations of the Marmara region. The compressive strength's results obtained by changing the aggregates were compared by ensuring the other components forming the concrete remained constant. 330 separate experiments were conducted to determine the physical characteristics of the aggregates, and these characteristics were used as input data in a multi layer Feed Forward Network model. The compressive strength of 7 and 28 days of the concrete obtained from the experiments were used as output in to a Multi-layer Feed Forward model. The training and test results from the models coincided closely with experimental results; also, the results were compared to the estimations made with a linear regression method.

KEYWORDS
Aggregate, Multi Layer Feed Forward Neural Network, Compressive Strength

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Okan Özbakır , Erkin Nasuf , "Modelling of Multi Layer Feed forward Neural Networks to Determine the Compressive Strength of Marmara Region Aggregate's Concrete," Universal Journal of Geoscience, Vol. 4, No. 2, pp. 23 - 30, 2016. DOI: 10.13189/ujg.2016.040202.

(b). APA Format:
Okan Özbakır , Erkin Nasuf (2016). Modelling of Multi Layer Feed forward Neural Networks to Determine the Compressive Strength of Marmara Region Aggregate's Concrete. Universal Journal of Geoscience, 4(2), 23 - 30. DOI: 10.13189/ujg.2016.040202.