Skip to main content

Table 1 Samples of the input data sets for the training 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 aggregate (%) Coarse aggregate (%) Unit weight (kg/m3) Water content (%) Temperature (°C)
Water Cement Fine aggregate Coarse aggregate Fly ash Silica fume
Harmathy (1983) 0.6 0.50 0 0 510 1,020 0 0 0 0 0 33
0.7 0.50 0 0 510 1,020 0 0 0 0 0 100
0.9 0.33 0 0 432 1,308 0 0 0 0 0 310
1.0 0.33 0 0 432 1,308 0 0 0 0 0 35
1.1 0.44 31 46 170 387 736 1,115 0 0 0 22
1.3 0.44 31 46 170 387 736 1,115 0 0 0 150
1.3 0.49 30 46 190 385 701 1,096 0 0 0 305
1.4 0.49 30 46 190 385 701 1,096 0 0 0 20
1.6 0.62 29 51 183 294 701 1,236 0 0 0 165
1.7 0.62 29 51 183 294 701 1,236 0 0 0 30
Morabito (1989) 1.1 0.50 46 16 175 350 700 250 0 0 100 20
1.2 0.50 57 0 292 583 1,167 0 0 0 0 20
1.3 0.50 36 32 175 350 700 250 0 0 100 20
1.7 0.50 23 60 175 350 700 1,850 0 0 100 20
1.8 0.50 30 48 175 350 700 250 0 0 0 20
Yamazaki et al. (1995) 1.1 0.48 30 51 145 242 707 1,204 60 0 0 385
1.1 0.48 30 51 145 242 707 1,204 60 0 0 435
1.2 0.48 30 51 145 242 707 1,204 60 0 0 295
1.2 0.48 30 51 145 242 707 1,204 60 0 0 345
1.3 0.48 30 51 145 242 707 1,204 60 0 0 245
Lie and Kodur (1996) 1.0 0.37 26 48 161 439 621 1,128 0 0 0 385
1.4 0.37 26 48 161 439 621 1,128 0 0 0 32
1.4 0.37 26 48 161 439 621 1,128 0 0 0 190
1.5 0.37 26 48 161 439 621 1,128 0 0 0 75
1.5 0.37 26 48 161 439 621 1,128 0 0 0 110
Khan et al. (1998) 1.1 0.50 33 44 178 355 790 1,040 0 0 0 37
1.2 0.50 33 44 178 355 790 1,040 0 0 0 25
1.7 0.25 29 44 133 494 720 1,105 0 46 0 82
1.8 0.25 29 44 133 494 720 1,105 0 46 0 73
Van Geem et al. (1997) 1.6 0.22 24 43 144 564 593 1,068 0 89 0 30
1.7 0.29 27 44 160 475 659 1,068 59 24 0 30
1.9 0.29 28 44 155 487 676 1,068 0 47 0 30
2.2 0.23 24 43 151 475 593 1,068 104 74 0 149
2.3 0.28 26 44 158 564 647 1,068 0 0 0 30
Khan et al. (1998) 1.9 0.60 56 0 191 318 635 0 0 0 0 25
2.0 0.60 26 53 191 318 635 1,270 0 0 0 25
2.2 0.60 27 52 191 318 635 1,256 0 0 0 25
2.3 0.60 26 53 191 318 635 1,274 0 0 0 25
2.3 0.60 27 52 191 318 637 1,260 0 0 0 25
Kodur and Sultan (2003) 1.0 0.26 28 44 140 500 700 1,100 0 50 0 600
1.2 0.26 28 44 140 500 700 1,100 0 50 0 500
1.8 0.26 28 44 140 500 700 1,100 0 50 0 200
1.9 0.26 28 44 140 500 700 1,100 0 50 0 100
2.0 0.26 28 44 140 500 700 1,100 0 50 0 23
Kim et al. (2003) 1.1 0.35 0 0 524 1,498 0 0 0 0 100 60
1.2 0.25 0 0 440 1,762 0 0 0 0 100 20
1.2 0.30 0 0 486 1,619 0 0 0 0 100 40
1.4 0.40 10 16 420 1,050 206 321 0 0 100 60
1.6 0.40 17 26 340 850 345 546 0 0 100 60
1.9 0.40 25 40 220 550 559 880 0 0 100 60
2.0 0.40 25 40 220 550 559 880 0 0 100 40
2.4 0.40 39 32 181 452 887 729 0 0 100 20
2.4 0.40 28 44 181 452 630 989 0 0 100 40
2.5 0.40 31 48 140 350 702 1,103 0 0 100 40