Effective Prediction of Thermal Conductivity of Concrete Using Neural Network Method

International Journal of Concrete Structures and Materials

Table 2 Experiment data sets for the evaluation of the neural network.

Researchers	TC (W/m K)	Water–cement ratio	Fine agg. percentage	Coarse agg. percentage	Unit weight (kg/m³)						Water content (%)	Temperature (°C)
Researchers	TC (W/m K)	Water–cement ratio	Fine agg. percentage	Coarse agg. percentage	Water	Cement	Fine aggregate	Coarse aggregate	Fly ash	Silica fume	Water content (%)	Temperature (°C)
Harmathy (1983)	0.3	0.50	0	0	510	1,020	0	0	0	0	0	660
	0.7	0.25	0	0	392	1,568	0	0	0	0	0	560
	0.9	0.25	0	0	392	1,568	0	0	0	0	0	100
	1.2	0.44	31	46	170	387	736	1,115	0	0	0	280
	1.3	0.62	29	51	183	294	701	1,236	0	0	0	330
	1.5	0.49	30	46	190	385	707	1,096	0	0	0	155
Yamazaki et al. (1995)	1.0	0.48	30	51	145	242	707	1,204	60	0	0	485
Yamazaki et al. (1995)	1.3	0.48	30	51	145	242	707	1,204	60	0	0	195
Khan et al. (1998)	1.6	0.30	34	42	135	421	820	1,025	0	34	0	28
Khan et al. (1998)	1.8	0.25	29	44	133	494	720	1,105	0	46	0	25
Lie and Kodur (1996)	1.0	0.37	26	48	161	439	621	1,128	0	0	0	490
Lie and Kodur (1996)	1.2	0.37	26	48	161	439	621	1,128	0	0	0	290
Van Geem et al. (1997)	1.8	0.29	27	43	160	475	359	1,068	59	24	0	370
	1.9	0.22	24	43	144	564	593	1,068	0	89	0	150
	2.0	0.29	28	44	155	487	676	1,068	0	47	0	150
	2.1	0.23	24	43	151	475	593	1,068	104	74	0	30
Kodur and Sultan (2003)	1.4	0.26	28	44	140	500	700	1,100	0	50	0	400
Kodur and Sultan (2003)	1.6	0.26	28	44	140	500	700	1,100	0	50	0	300
Kim et al. (2003)	1.0	0.30	0	0	486	1,619	0	0	0	0	0	20
	1.3	0.30	0	0	486	1,619	0	0	0	0	100	20
	1.3	0.40	17	26	340	850	345	546	0	0	0	40
	1.4	0.40	10	16	420	1,050	206	321	0	0	100	20
	1.7	0.40	17	26	340	850	345	546	0	0	100	40
	1.8	0.40	23	35	260	650	490	768	0	0	100	60
	1.8	0.40	28	44	181	452	630	989	0	0	0	60
	2.2	0.40	28	44	181	452	630	989	0	0	100	60
	2.3	0.40	28	44	181	452	630	989	0	0	100	20
	2.4	0.40	36	36	181	452	810	806	0	0	100	20