Table 6 Comparison of the proposed model in this study with literature.

From: Comparative performance evaluation of machine learning models for predicting the ultimate bearing capacity of shallow foundations on granular soils

Approach

Error measures

R (%)

RMSE

MAE

SI (%)

Ia

DR (mean ± std)

Terzaghi (1943)

84.9

264.49

160.36

56.46

0.91

0.99 ± 0.41

Meyerhof (1963)

93.5

163.142

108.33

34.82

0.96

0.85 ± 0.28

Hansen (1970)

94.3

211.17

140.83

45.07

0.92

0.69 ± 0.25

Vesic (1975)

93.6

164.94

111.07

35.2

0.96

0.84 ± 0.28

Shahnazari-MLR (2012)

72

349.86

292.2

74.68

0.82

0.80 ± 1.61

Shahnazari-MGP (2012)

93.7

256.72

132.83

54.8

0.94

0.84 ± 0.39

Tsai-GP (2012)

73.2

522.19

242.31

109.46

0.45

0.75 ± 0.27

Sadrossadat-LGP (2013)

95.9

177.99

120.11

37.99

0.96

0.72 ± 0.37

Omar-ANN (2018)

94.6

27.434

11.117

-

-

-

Khorrami-M5 (2020)

96

130.14

86.08

27.78

0.98

0.99 ± 0.23

Zhang and Xue- MEP (2022)

88

-

-

-

-

-

Alzabeebee. S., et. al.EPR-MOGA19

98.48

110.5

61.5

-

-

-

Kumar-ANN-ICA(2024)

95.47

0.164

0.133

-

-

-

AdaBoost - This study

95.35

115.534

54.500

-

-

-

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