Advances in Bridge Engineering

Table 2 Data used in neural network

From: Prediction of the compressive strength of lightweight concrete containing industrial and waste steel fibers using a multilayer synthetic neural network

Mix number	component
Mix number	Cement (kg/m3)	Steel fibers (kg/m3)	Water (kg/m3)	Gravel (kg/m3)	Sand (kg/m3)	Super lubricant (kg/m3)
1	350	0	105	1180	850	1/4
2	332/5	17/5	105	1150	850	1/5
3	315	35	105	1150	850	2/52
4	297/5	52/5	105	1150	850	2/87
5	280	70	105	1150	850	3/1
6	350	0	140	1100	820	1/05
7	332/5	17/5	140	1100	820	1/17
8	315	35	140	1100	820	1/75
9	297/5	52/5	140	1100	820	2/34
10	280	70	140	1100	820	2/8
11	350	0	175	1015	810	0
12	332/5	17/5	175	1015	810	0/24
13	315	35	175	1015	810	0/48
14	297/5	52/5	175	1015	810	0/84
15	280	70	175	1015	810	2/04
16	400	0	120	1100	850	1/82
17	380	20	120	1080	850	2/64
18	360	40	120	1080	850	3/68
19	340	60	120	1080	850	4/02
20	320	80	120	1080	850	5/29
21	400	0	160	1000	830	0/7
22	380	20	160	1000	820	0/88
23	360	40	160	1000	820	1/06
24	360	40	160	1000	820	0/89
25	320	80	160	1000	820	2/25
26	340	80	0	980	750	2/25
27	320	0	20	980	750	0/24
28	400	20	20	980	750	0/24
29	380	40	40	980	750	0/6
30	360	60	60	980	750	0/96
31	340	80	80	980	750	1/33
32	320	0	0	1050	800	2/54
33	450	22	22/5	1050	800	6/36
34	427/5	45	45	1050	800	13/89
35	405	67/5	67/5	1050	800	16/21
36	382/5	90	90	1050	800	23/74
37	360	0	0	950	800	0/71
38	428/5	22/5	22/5	920	800	1/2
39	405	45	45	920	800	1/2
40	385	67	67/5	920	800	1/8
41	362	90	0	900	800	2/4
42	452	0	22/5	900	700	00
43	425	22/5	45	900	700	00
44	410	45	67/5	900	700	0/06
45	352	67	90	900	700	0/55

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