Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks

Table 4

Results obtained from MLP, FF, RBF, and TLRN optimal models in the training, validation, and test stages for compressive strength of concrete.

Test				Validation concurrent with training				Train				Model	Number
Best fitting line in testing phase		Performance criteria		Best fitting line in validation phase		Performance criteria		Best fitting line in training phase		Performance criteria
Equation		NMSE		Equation		NMSE		Equation		NMSE
	0.899	0.106	0.948		0.935	0.070	0.967		0.910	0.090	0.954	MLP	1
	0.843	0.179	0.918		0.916	0.126	0.957		0.794	0.206	0.891	FF	2
	0.711	0.380	0.843		0.783	0.379	0.885		0.734	0.381	0.857	RBF	3
	0.473	0.705	0.688		0.317	1.180	0.563		0.411	0.813	0.641	TLRN	4