Machine Learning-Based Model in Predicting the Plate-End Debonding of FRP-Strengthened RC Beams in Flexure

Table 3

Weights and biases between connection layers.

	Hidden layer						Output
	H(1 : 1)	H(1 : 2)	H(1 : 3)	H(1 : 4)	H(1 : 5)	H(1 : 6)	H(1 : 7)	V
Bias	.743	.529	2.692	.061	−1.476	.038	.776
X1	.022	1.514	.906	.179	.279	.443	−.473
X2	−.095	−.656	−.243	.438	.662	−.188	−1.296
X3	−.348	.946	−.998	−.395	.900	−.240	.362
X4	−.037	.765	−.727	.350	−.142	−.096	−1.060
X5	−.006	.782	1.731	.816	−.139	.281	−.277
X6	−.681	−.587	−.845	−.428	−.135	−.695	.584
X7	.272	1.773	−.444	.053	.583	−.030	1.619
X8	1.225	.077	−.300	.169	.669	.940	.993
Bias								1.115
H(1 : 1)								−1.425
H(1 : 2)								.381
H(1 : 3)								−2.224
H(1 : 4)								.511
H(1 : 5)								−1.377
H(1 : 6)								1.147
H(1 : 7)								.736