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

From: Effective Prediction of Thermal Conductivity of Concrete Using Neural Network Method

Researchers TC (W/m K) Water–cement ratio Fine agg. percentage Coarse agg. percentage Unit weight (kg/m3) Water content (%) Temperature (°C)
Water Cement Fine aggregate Coarse aggregate Fly ash Silica fume
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
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
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
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
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