Suphi URAL, Murat AYDIN

Prediction of the Rapid Hardening Property of Calcium Aluminate Cement Based on Mineralogical Composition by Neural Network

Quantitative X-ray diffractometry using a Rietveld-based computational method was carried out for a series of Calcium Aluminate Cement (CAC) samples. This indicated that the CA content ranged between 37.7% to 47.7% while Brownmillerite (C4AF) amount varies between 11.0% to 23.6%. Magnetite was found in all the samples, ranging from 0.7% to 3.9% while Gehlenite amount varies between 0.5% and 6.5%. The Spinel amount was between 0.1% to 1.3% with an average of 0.5%. The amorphous content of CAC is ranged between 12.0% and 32%. The Mayenite and amorphous content could be a good indicator of the Rapid Hardening (RH) property of CAC. Samples with the high Mayenite content showed less RH properties, whereas RH increased as the content of amorphous material increased. The RH properties of CAC based on its mineralogical composition was predicted through various neural network techniques. The R2-value of the models was 0.39 for Linear Regression analysis model (LR), 0.56 for feed forward neural network (ANN) and 0.78 for Generalized Regression Neural Network (GRNN) approaches. The best prediction approach for RH value of the CAC with an Al2O3 content of 40% was GRNN that can be applied to predict RH.

Anahtar Kelimeler:

CAC, GRNN, neural network, rapid hardening, Rietveld, CAC, GRNN, neural network, rapid hardening, Rietveld

Prediction of the Rapid Hardening Property of Calcium Aluminate Cement Based on Mineralogical Composition by Neural Network

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