Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria-Based Concrete

Table 5

Weight matrix and weights between input and hidden layers (W1) and between hidden and output layers (W2) for the ANN1 model.

Neuron	W1					W2
	Input					Output
	Time (day)	Cement content (kg)	VS content (kg)	Water content (kg)	SP
1	−2.105944022	1.075228351	1.255199036	0.438755839	−0.030683697	1.279718465
2	0.144053427	−0.972020695	0.742996724	1.753640631	−1.340918263	−1.208902522
3	1.732934016	0.61985474	−1.155641212	−1.283690471	3.243864115	0.460723363
4	0.903445767	0.372764295	1.572839487	−0.87906307	1.904073089	0.705296001
5	0.938501968	−1.091473696	−2.116933639	−0.459056052	1.573936415	1.53887134
6	0.344253977	−0.597794971	0.779210827	1.235683476	2.059459437	0.961634634
7	−1.024890536	−1.466697926	−1.591821924	−1.081779779	1.829483202	0.698867474
8	−0.064928174	−1.591852569	1.716382139	1.62579402	−2.437814145	−0.331804241
9	−1.498209432	0.875620108	−0.423664145	−1.895854904	2.039710262	0.545700344
10	−0.233072918	−1.362387421	1.333842112	−1.465558	1.098041189	−0.562090401
11	−0.936088182	2.726784678	−1.120598625	−2.150579249	−0.530783246	0.108067552
12	−11.888422	0.182734218	0.268691589	0.167278196	0.141190095	−4.372180414
13	−0.214986128	−1.611586142	1.972556139	−1.4704395	1.201024837	−0.399869245
14	1.564288124	0.63694431	1.050612614	−0.532653652	0.778724302	0.192780767
15	0.430315807	−0.418860675	0.04929488	−0.345348435	−1.723849754	2.899735928
16	−0.007106844	−1.904352783	−1.119167674	1.319502798	0.223633621	2.309346307