TY  - JOUR
T1  - Fiber Cement Composition Simulator Using Artificial Neural Networks
AU - , A.C.S. Silva AU - , E.M. Bezerra AU - , E.J.X. Costa AU - , H. Savastano 
JO  - Journal of Engineering and Applied Sciences
VL  - 2
IS  - 7
SP  - 1206
EP  - 1212
PY  - 2007
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2007.1206.1212
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2007.1206.1212
KW  - Mixture proportioning
KW  -mechanical properties
KW  -physical properties
KW  -composite
KW  -fiber reinforcement
KW  -artificial neural network
AB  - The backpropagation algorithm was utilized to implement a fiber cement composition simulator. Six predictors were used: Synthetic fiber supplier, content of synthetic fiber, supplier of the softwood cellulose pulp, refinement degree of softwood cellulose pulp, content of softwood cellulose pulp and refinement degree of  hardwood  cellulose pulp. The combination of the 6 predictors generated compositions that were used as the Artificial Neural Network (ANN) target in relation to the variables: modulus of rupture (<I>y</I><SUB>1</SUB>), toughness (<I>y</I><SUB>2</SUB>) and water absorption (<I>y</I><SUB>3</SUB>) of the fiber cement composites at the total age of 28 days that were used as the neural network input. The ANN performance was 97.3 % of correct classification with kappa coefficients varying between 0.89 and 0.93. The results suggest that the ANN approach can be used to simulate the composite formulation based on mechanical and physical characteristics using historical data set from experimental results.
ER  -