Genetic algorithm based ANN to predict compressive strength of siphon for different fiber volume fraction - Santosh Kumar, Gottapu; Rajasekhar, K (International Journal of Engineering and Technology, 2018)
This paper presents the applicability of Genetic Algorithm based Artificial Neural Network (GAANN) for predicting Compressive strength of Slurry Infiltrated Fibrous Concrete (SIFCON) prepared with manufactured sand for different fibre volume fraction (8%, 10% and 12%) as input vector. The network has been trained with data obtained from experimental work. The proposed GAANNs model is successfully used for predicting compressive strength of SIFCON (output vector) for various fibre volume fractions (2%, 4%, 6%, 14%, 16%, 18%, 20% and 22%) at 7 days, 28 days and 56 days of curing respectively. After successful learning GA based ANN model pre- dicted the compressive strength property satisfying all the constrains with an accuracy of about 85%.The various stages involved in the development of genetic algorithm based neural network are addressed in depth in this paper.Â Â