Feed Forward Back Propagation Artificial Neural Network Modeling of Compressive Strength of Self-Compacting Concrete
2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)
(2018), pp. 1-5
Stephen John C. Clemente
a
,
Mary Grace M. Ventanilla
a
,
Elmer P. Dadios
b
,
Andres Winston C. Oreta
b
a College of Engineering, FEU Institute of Technology, Manila, Manila, Philippines
b Gokongwei College of Engineering, De La Salle University, Manila, Manila, Philippines
Abstract: Predicting the compressive strength of self-compacting concrete (SCC) is one of the complicated tasks because of its complex behavior due to the reaction of chemical and mineral content and the hydration process of cement. The distinct difference of SCC to normal concrete is its improved workability or also known as rheology that is divided into four categories namely viscosity, flow ability, passing ability, and resistance to segregation. It was proposed in this study to include the rheological behavior of SCC to the prediction of its compressive strength. Neural network was utilized for predicting the 28th day compressive strength of SCC. A 97.78% prediction rate was achieved using a feed-forward back-propagation ANN with 1 hidden layer and 8 hidden nodes. Tansig transfer function was used as activation function. The model has a Pearson R value of 0.991 and mean square error (MSE) of 3.42.