Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

Table 7

Results of model 1 (ANN) and model 2 (GP).

Model 1:		Training of the dataset
Artificial Neural Network (ANN)		Epochs taken	Coefficient of determination ()	Root mean square error (RMSE)
Result number	Curing time
R1 (without fly ash)	28 days	04	0.898	6.9762e − 006
	56 days	05	0.998	1.2712e − 007
	91 days	03	01	7.3640e − 009
R2 (with 0.15 fly ash)	28 days	05	0.996	3.8809e − 007
	56 days	04	01	3.6873e − 009
	91 days	04	01	2.2181e − 010
Model 2: Genetic Programming (GP)
	28 days	Not applicable	0.77438	0.01067
R3 (without fly ash)	56 days		0.99999	0.00550
	91 days		0.99999	0.00644
	28 days		0.93781	0.01415
R4 (with 0.15 fly ash)	56 days		0.94483	0.00910
	91 days		0.96681	0.00689