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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