On the Training Algorithms for Artificial Neural Network in Predicting the Shear Strength of Deep Beams

Table 6

Comparison of prediction performance between this investigation and currently popular AI techniques.

Model	Statistical criteria
	R	RMSE	MAE	MAPE
Genetic-simulated annealing (GSA) [4]	0.929	—	—	—
Backpropagation neural network (BPNN) [42]	0.916	34.032	—	11.273
Radial basis function neural network (RBFNN) [42]	0.9767	20.29	—	7.63
Artificial neural network (ANN) [43]	0.9711	42.27	30.28	—
Gene expression programming (GEP) [43]	0.9654	51.57	40.99	—
Support vector machine (SVM) [42]	0.9465	30.134	—	14.435
Multivariate adaptive regression splines (EMARS) [42]	0.986	13.011	—	5.887
Genetic-simulated annealing (GSA) [4]	0.929	—	—	12.3
Smart artificial firefly colony algorithm and least squares support vector regression (SFA LS-SVR) [24]	0.941	—	—	8.87
Adaptive neural fuzzy inference system (ANFIS) [44]	0.984	34.76	25.24	—
Artificial neural network-conjugate gradient (ANN-CG) of this study	0.992	14.02	11.24	6.84