Table 9 Four new test data-points with various aggregate sizes to evaluate the effect of this parameter on the CS and examine the performance of machine learning techniques.

From: Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete

Fiber type

Fiber content (%)

Fiber length (mm)

Fiber diameter (mm)

w/c

Curing time (days)

Silica fume (%)

Aggregate size (mm)

Superplasticizer (kg/m3)

Glass

1.25

18

0.52

0.45

28

7.3

10

2.4

Glass

1.25

18

0.52

0.45

28

7.3

11

2.4

Glass

1.25

18

0.52

0.45

28

7.3

12

2.4

Glass

1.25

18

0.52

0.45

28

7.3

13

2.4

Glass

1.25

18

0.52

0.45

28

7.3

14

2.4

Glass

1.25

18

0.52

0.45

28

7.3

15

2.4

Glass

1.25

18

0.52

0.45

28

7.3

16

2.4

Glass

1.25

18

0.52

0.45

28

7.3

17

2.4

Glass

1.25

18

0.52

0.45

28

7.3

18

2.4

Glass

1.25

18

0.52

0.45

28

7.3

19

2.4

Glass

1.25

18

0.52

0.45

28

7.3

20

2.4

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