Shear Resistance Prediction of Post-fire Reinforced Concrete Beams Using Artificial Neural Network

Cai, Bin; Xu, Long-Fei; Fu, Feng

doi:10.1186/s40069-019-0358-8

International Journal of Concrete Structures and Materials

Table 2 Predicted data.

From: Shear Resistance Prediction of Post-fire Reinforced Concrete Beams Using Artificial Neural Network

No.	t (min)	b (mm)	h (mm)	A _sv (mm²)	f _t (MPa)	s (mm)	c (mm)	\(V_{target}\) (kN)	\(V_{output}\) (kN)	e (%)
1	5	220	500	100.53	2.8	150	25	307.07	305.58	0.48
2	10	220	500	100.53	2.8	150	25	302.59	301.56	0.34
3	15	220	500	100.53	2.8	150	25	296.89	297.66	0.26
4	20	220	500	100.53	2.8	150	25	291.69	293.88	0.75
5	25	220	500	100.53	2.8	150	25	287.58	290.23	0.92
6	30	220	500	100.53	2.8	150	25	283.85	286.73	1.01
7	35	220	500	100.53	2.8	150	25	280.76	283.41	0.95
8	40	220	500	100.53	2.8	150	25	278.54	280.32	0.64
9	45	220	500	100.53	2.8	150	25	276.6	277.5	0.32
10	50	220	500	100.53	2.8	150	25	275.13	274.98	0.05
11	55	220	500	100.53	2.8	150	25	273.74	272.84	0.33
12	60	220	500	100.53	2.8	150	25	272.44	271.09	0.5
13	65	220	500	100.53	2.8	150	25	271.24	269.75	0.55
14	70	220	500	100.53	2.8	150	25	270.11	268.79	0.49
15	75	220	500	100.53	2.8	150	25	269.08	268.11	0.36
16	80	220	500	100.53	2.8	150	25	268.17	267.5	0.25
17	85	220	500	100.53	2.8	150	25	267.39	266.63	0.29
18	90	220	500	100.53	2.8	150	25	266.72	265.07	0.62
19	95	220	500	100.53	2.8	150	25	262.29	262.35	0.02
20	100	220	500	100.53	2.8	150	25	256.13	258.15	0.79
21	105	220	500	100.53	2.8	150	25	250.33	252.44	0.84
22	110	220	500	100.53	2.8	150	25	244.88	245.56	0.28
23	115	220	500	100.53	2.8	150	25	239.64	238.04	0.67
24	120	220	500	100.53	2.8	150	25	234.13	230.32	1.63
·	·	·	·	·	·	·	·	·	·	·
·	·	·	·	·	·	·	·	·	·	·
·	·	·	·	·	·	·	·	·	·	·
73	5	300	650	100.53	2.8	150	25	517.84	512.19	1.09
74	10	300	650	100.53	2.8	150	25	513.44	506.12	1.43
75	15	300	650	100.53	2.8	150	25	505.3	499.1	1.23
76	20	300	650	100.53	2.8	150	25	494.87	491.12	0.76
77	25	300	650	100.53	2.8	150	25	484.59	482.28	0.48
78	30	300	650	100.53	2.8	150	25	474.97	472.8	0.46
79	35	300	650	100.53	2.8	150	25	463.3	463.05	0.05
80	40	300	650	100.53	2.8	150	25	452.13	453.5	0.3
81	45	300	650	100.53	2.8	150	25	442.64	444.66	0.46
82	50	300	650	100.53	2.8	150	25	433.83	436.84	0.69
83	55	300	650	100.53	2.8	150	25	427.54	430.04	0.59
84	60	300	650	100.53	2.8	150	25	420.82	423.87	0.73
85	65	300	650	100.53	2.8	150	25	414.41	417.6	0.77
86	70	300	650	100.53	2.8	150	25	408.41	410.47	0.5
87	75	300	650	100.53	2.8	150	25	402.16	401.9	0.07
88	80	200	650	100.53	2.8	150	25	388.9	391.68	0.72
89	85	200	650	100.53	2.8	150	25	375.81	380.04	1.13
90	90	200	650	100.53	2.8	150	25	367.03	367.55	0.14
91	95	200	650	100.53	2.8	150	25	353.82	354.97	0.33
92	100	200	650	100.53	2.8	150	25	345.42	343.07	0.68
93	105	200	650	100.53	2.8	150	25	333.43	332.49	0.28
94	110	200	650	100.53	2.8	150	25	325.89	323.66	0.68
95	115	200	650	100.53	2.8	150	25	319.42	316.84	0.81
96	120	200	650	100.53	2.8	150	25	314.84	312.14	0.86

\(V_{target}\) and \(V_{output}\) were the target value and predicted value of the reinforced concrete, respectively; \(e = \left| {V_{target} - V_{output} } \right|/V_{target}\).

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