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

Table 5

Architecture selected for model 1 (ANN model).

Parameters	Values	Description
Dataset	Dataset 1: 49 (without fly ash); Dataset 2: 27 (with fly ash)	Dataset is of two types. One is without any substitution of cement by fly ash and the second one is with 15% of the cement replaced by fly ash. Dataset 1 is of 49 tuples in total. Dataset 2 is of 27 tuples in total
Number of input parameters	04 (cement (C), water (W), fine aggregate (sand), coarse aggregate (CA)); in case of 05 (cement (C), water (W), fine aggregate (sand), coarse aggregate (CA), 28-day compressive strength (CS28)); in case of 06 (cement (C), water (W), fine aggregate (sand), coarse aggregate (CA), 28-day compressive strength (CS28), 56-day compressive strength (CS56)). Fly ash (FA) is used with dataset 2 only; all other parameters are the same as dataset 1	When output is 28 days, then the number of input parameters is 04; when output is 56 days, the number of input parameters is 05 as 28-day compressive strength is taken as input; when output is 91 days, the number of input parameters is 06 as 28-day compressive strength and 56-day compressive strength are also taken as input. In dataset 2, FA is replacing 15% of the cement
Activation function 1	tansig()
Activation function 2	purelin()	purelin() =
Performance function	MSE	,; where is the input patterns
Net.trainparam.lr	0.01	Learning rate
Net.trainfcn	trainlm	Levenberg-Marquardt algorithm
Net.trainparam.epochs	10000	Maximum number of epochs to train
Net.trainparam.goal	0.000001	Performance goal
Number of hidden layer neurons	50	—
Number of output layer neurons	1	—