# Surrogate Model Development for Slope Stability Analysis Using Machine Learning

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

^{−4}, which indicates a strong relationship between the FOS values and the selected slope parameters. The significant difference in the elapsed time between the traditional method and the developed surrogate model for slope stability analysis highlights the potential benefits of machine learning.

## 1. Introduction

## 2. Dataset

#### 2.1. Modeling: A Simple and Homogeneous Soil Slope

^{3}, 14 MPa, and 0.3, respectively. To ensure the validity of the analysis, a range of shear strength properties are utilized, and both numerical simulations using FLAC 3D and analyses using the limit equilibrium method (LEM) were conducted for the parametric study. The FOS results are listed in Table 1.

#### 2.2. An Established Dataset

^{3}, a Young’s modulus of 14 MPa, a Poisson’s ratio of 0.3, and a tensile strength of 0. Four key parameters were considered to represent the slope characteristics: slope height (H), slope angle (α), cohesion (c), and internal friction angle (φ). Values of 3 m, 6 m, and 9 m were considered for the slope height, and values of 26.57°, 45°, and 63.43° were used for the slope angle. For the soil parameters (shear strength), a range of 2–50 kPa was considered for soil cohesion, and a range of 0–45° was considered for the internal friction angle. This resulted in a dataset consisting of 880 homogenous slopes with FOS values as the output. The generated dataset is shown in Appendix A Table A1. The dataset was then used for the classification model for slopes and the regression model for FOS prediction, which are introduced in Section 3, model development. Note that the dataset used in this study was selected solely by the simple full factorial experiment method, without considering data balance. In addition, the decision to use homogeneous slopes in this study was a deliberate choice made to establish a clear understanding of the neural network’s behavior and performance under simplified conditions.

## 3. Model Development

#### 3.1. A Deep Neural Network Model for Slope Classification

- (a)
- Data preprocessing: The first step is to prepare the input data and target labels used to train the network. This typically involves dividing the data into training and test sets.
- (b)
- Network construction and training: The next step is to train the network using the training data. A DNN model is trained on the input data, which requires specifying the input data, target labels, and the type of network to be trained.
- (c)
- Prediction: Once the network is trained, the model can be used to predict the test set.
- (d)
- Performance evaluation: The performance of the network can be evaluated using a confusion matrix.

#### 3.2. A Deep Neural Network Model for the Factor of Safety Regression

- (a)
- Data preprocessing: The dataset is divided into training and test sets, and necessary preprocessing steps such as feature normalization and the handling of missing values are performed.
- (b)
- Network construction and training: A DNN consisting of input, hidden, and output layers is constructed, with the architecture of the network determined by factors such as the number of hidden layers and nodes. The network is trained using a training set.
- (c)
- Prediction: The trained model is utilized to generate predicted values for new input data.
- (d)
- Performance evaluation: The performance of the model was evaluated using metrics such as the mean squared error (MSE) and correlation coefficient R-square value.

## 4. Results and Discussion

#### 4.1. Slope Classification

#### 4.2. Slope FOS Prediction

^{−4}, and the correlation coefficient R-square for the regression model was 0.9989, indicating a strong linear relationship between the predicted FOS value and the true FOS obtained by numerical simulation. This means that this regression model can be used to predict FOS accurately. Thus, the regression model accurately predicted the FOS value and effectively modeled the relationship between the FOS values and the selected slope parameters.

#### 4.3. Time Consumption

#### 4.4. Discussion

#### 4.5. Contributions, Limitations, and Further Research

## 5. Conclusions

^{−4}, indicating a strong relationship between the FOS values and the selected slope parameters. However, the generated dataset may not be representative of all the actual site conditions, and more complex geological conditions and other input factors must be considered. Moreover, such a surrogate model can complement traditional computational methods and accelerate the prediction of the FOS in slope stability analysis. This capability enables decision makers to promptly take the necessary actions to prevent and mitigate potential hazards, thus contributing to the development of effective and efficient risk management strategies. Incorporating surrogate models into slope stability problems can effectively achieve long-term sustainability while minimizing risks and preserving natural resources.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

Case No. | Slope Height/m | Slope Angle/° | Cohesion/kPa | Friction Angle/° | FOS | Labels |
---|---|---|---|---|---|---|

1 | 3 | 26.57 | 2 | 5 | - | U |

2 | 3 | 26.57 | 2 | 10 | - | U |

3 | 3 | 26.57 | 2 | 15 | - | U |

4 | 3 | 26.57 | 2 | 20 | - | U |

5 | 3 | 26.57 | 2 | 25 | - | U |

6 | 3 | 26.57 | 2 | 30 | - | U |

7 | 3 | 26.57 | 2 | 35 | - | U |

8 | 3 | 26.57 | 2 | 40 | - | U |

9 | 3 | 26.57 | 2 | 45 | - | U |

10 | 3 | 26.57 | 5 | 5 | - | U |

11 | 3 | 26.57 | 5 | 10 | - | U |

12 | 3 | 26.57 | 5 | 15 | - | U |

13 | 3 | 26.57 | 5 | 20 | - | U |

14 | 3 | 26.57 | 5 | 25 | - | U |

15 | 3 | 26.57 | 5 | 30 | - | U |

16 | 3 | 26.57 | 5 | 35 | - | U |

17 | 3 | 26.57 | 5 | 40 | - | U |

18 | 3 | 26.57 | 5 | 45 | - | U |

19 | 3 | 26.57 | 10 | 5 | - | U |

20 | 3 | 26.57 | 10 | 10 | - | U |

21 | 3 | 26.57 | 10 | 15 | - | U |

22 | 3 | 26.57 | 10 | 20 | - | U |

23 | 3 | 26.57 | 10 | 25 | - | U |

24 | 3 | 26.57 | 10 | 30 | - | U |

25 | 3 | 26.57 | 10 | 35 | 1.04 | M |

26 | 3 | 26.57 | 10 | 40 | 1.12 | M |

27 | 3 | 26.57 | 10 | 45 | 1.2 | M |

28 | 3 | 26.57 | 15 | 5 | - | U |

29 | 3 | 26.57 | 15 | 10 | - | U |

30 | 3 | 26.57 | 15 | 15 | 1.04 | M |

31 | 3 | 26.57 | 15 | 20 | 1.12 | M |

32 | 3 | 26.57 | 15 | 25 | 1.2 | M |

33 | 3 | 26.57 | 15 | 30 | 1.28 | S |

34 | 3 | 26.57 | 15 | 35 | 1.36 | S |

35 | 3 | 26.57 | 15 | 40 | 1.46 | S |

36 | 3 | 26.57 | 15 | 45 | 1.54 | S |

37 | 3 | 26.57 | 20 | 5 | 1.14 | M |

38 | 3 | 26.57 | 20 | 10 | 1.23 | S |

39 | 3 | 26.57 | 20 | 15 | 1.31 | S |

40 | 3 | 26.57 | 20 | 20 | 1.4 | S |

41 | 3 | 26.57 | 20 | 25 | 1.49 | S |

42 | 3 | 26.57 | 20 | 30 | 1.57 | S |

43 | 3 | 26.57 | 20 | 35 | 1.67 | S |

44 | 3 | 26.57 | 20 | 40 | 1.76 | S |

45 | 3 | 26.57 | 20 | 45 | 1.86 | S |

46 | 3 | 26.57 | 25 | 5 | 1.4 | S |

47 | 3 | 26.57 | 25 | 10 | 1.49 | S |

48 | 3 | 26.57 | 25 | 15 | 1.58 | S |

49 | 3 | 26.57 | 25 | 20 | 1.68 | S |

50 | 3 | 26.57 | 25 | 25 | 1.76 | S |

51 | 3 | 26.57 | 25 | 30 | 1.85 | S |

52 | 3 | 26.57 | 25 | 35 | 1.95 | S |

53 | 3 | 26.57 | 25 | 40 | 2.05 | S |

54 | 3 | 26.57 | 25 | 45 | 2.17 | S |

55 | 3 | 26.57 | 30 | 5 | 1.67 | S |

56 | 3 | 26.57 | 30 | 10 | 1.76 | S |

57 | 3 | 26.57 | 30 | 15 | 1.85 | S |

58 | 3 | 26.57 | 30 | 20 | 1.93 | S |

59 | 3 | 26.57 | 30 | 25 | 2.04 | S |

60 | 3 | 26.57 | 30 | 30 | 2.13 | S |

61 | 3 | 26.57 | 30 | 35 | 2.23 | S |

62 | 3 | 26.57 | 30 | 40 | 2.34 | S |

63 | 3 | 26.57 | 30 | 45 | 2.46 | S |

64 | 3 | 26.57 | 35 | 5 | 1.91 | S |

65 | 3 | 26.57 | 35 | 10 | 2.02 | S |

66 | 3 | 26.57 | 35 | 15 | 2.12 | S |

67 | 3 | 26.57 | 35 | 20 | 2.21 | S |

68 | 3 | 26.57 | 35 | 25 | 2.3 | S |

69 | 3 | 26.57 | 35 | 30 | 2.4 | S |

70 | 3 | 26.57 | 35 | 35 | 2.51 | S |

71 | 3 | 26.57 | 35 | 40 | 2.62 | S |

72 | 3 | 26.57 | 35 | 45 | 2.74 | S |

73 | 3 | 26.57 | 40 | 5 | 2.16 | S |

74 | 3 | 26.57 | 40 | 10 | 2.29 | S |

75 | 3 | 26.57 | 40 | 15 | 2.38 | S |

76 | 3 | 26.57 | 40 | 20 | 2.48 | S |

77 | 3 | 26.57 | 40 | 25 | 2.57 | S |

78 | 3 | 26.57 | 40 | 30 | 2.68 | S |

79 | 3 | 26.57 | 40 | 35 | 2.79 | S |

80 | 3 | 26.57 | 40 | 40 | 2.9 | S |

81 | 3 | 26.57 | 40 | 45 | 3.02 | S |

82 | 3 | 26.57 | 45 | 5 | 2.41 | S |

83 | 3 | 26.57 | 45 | 10 | 2.55 | S |

84 | 3 | 26.57 | 45 | 15 | 2.64 | S |

85 | 3 | 26.57 | 45 | 20 | 2.74 | S |

86 | 3 | 26.57 | 45 | 25 | 2.83 | S |

87 | 3 | 26.57 | 45 | 30 | 2.93 | S |

88 | 3 | 26.57 | 45 | 35 | 3.07 | S |

89 | 3 | 26.57 | 45 | 40 | 3.18 | S |

90 | 3 | 26.57 | 45 | 45 | 3.3 | S |

91 | 3 | 26.57 | 50 | 5 | 2.69 | S |

92 | 3 | 26.57 | 50 | 10 | 2.8 | S |

93 | 3 | 26.57 | 50 | 15 | 2.9 | S |

94 | 3 | 26.57 | 50 | 20 | 3 | S |

95 | 3 | 26.57 | 50 | 25 | 3.1 | S |

96 | 3 | 26.57 | 50 | 30 | 3.2 | S |

97 | 3 | 26.57 | 50 | 35 | 3.33 | S |

98 | 3 | 26.57 | 50 | 40 | 3.45 | S |

99 | 3 | 26.57 | 50 | 45 | 3.58 | S |

100 | 3 | 26.57 | 2 | 0 | - | U |

101 | 3 | 26.57 | 5 | 0 | - | U |

102 | 3 | 26.57 | 10 | 0 | - | U |

103 | 3 | 26.57 | 15 | 0 | - | U |

104 | 3 | 26.57 | 20 | 0 | 1 | M |

105 | 3 | 26.57 | 25 | 0 | 1.25 | S |

106 | 3 | 26.57 | 30 | 0 | 1.5 | S |

107 | 3 | 26.57 | 35 | 0 | 1.76 | S |

108 | 3 | 26.57 | 40 | 0 | 2.02 | S |

109 | 3 | 26.57 | 45 | 0 | 2.27 | S |

110 | 3 | 26.57 | 50 | 0 | 2.51 | S |

111 | 3 | 45 | 2 | 5 | - | U |

112 | 3 | 45 | 2 | 10 | - | U |

113 | 3 | 45 | 2 | 15 | - | U |

114 | 3 | 45 | 2 | 20 | - | U |

115 | 3 | 45 | 2 | 25 | - | U |

116 | 3 | 45 | 2 | 30 | - | U |

117 | 3 | 45 | 2 | 35 | - | U |

118 | 3 | 45 | 2 | 40 | - | U |

119 | 3 | 45 | 2 | 45 | - | U |

120 | 3 | 45 | 5 | 5 | - | U |

121 | 3 | 45 | 5 | 10 | - | U |

122 | 3 | 45 | 5 | 15 | - | U |

123 | 3 | 45 | 5 | 20 | - | U |

124 | 3 | 45 | 5 | 25 | - | U |

125 | 3 | 45 | 5 | 30 | - | U |

126 | 3 | 45 | 5 | 35 | - | U |

127 | 3 | 45 | 5 | 40 | - | U |

128 | 3 | 45 | 5 | 45 | - | U |

129 | 3 | 45 | 10 | 5 | - | U |

130 | 3 | 45 | 10 | 10 | - | U |

131 | 3 | 45 | 10 | 15 | - | U |

132 | 3 | 45 | 10 | 20 | - | U |

133 | 3 | 45 | 10 | 25 | - | U |

134 | 3 | 45 | 10 | 30 | - | U |

135 | 3 | 45 | 10 | 35 | 1.04 | M |

136 | 3 | 45 | 10 | 40 | 1.12 | M |

137 | 3 | 45 | 10 | 45 | 1.2 | M |

138 | 3 | 45 | 15 | 5 | - | U |

139 | 3 | 45 | 15 | 10 | - | U |

140 | 3 | 45 | 15 | 15 | 1.04 | M |

141 | 3 | 45 | 15 | 20 | 1.13 | M |

142 | 3 | 45 | 15 | 25 | 1.21 | S |

143 | 3 | 45 | 15 | 30 | 1.28 | S |

144 | 3 | 45 | 15 | 35 | 1.36 | S |

145 | 3 | 45 | 15 | 40 | 1.44 | S |

146 | 3 | 45 | 15 | 45 | 1.54 | S |

147 | 3 | 45 | 20 | 5 | 1.13 | M |

148 | 3 | 45 | 20 | 10 | 1.23 | S |

149 | 3 | 45 | 20 | 15 | 1.31 | S |

150 | 3 | 45 | 20 | 20 | 1.41 | S |

151 | 3 | 45 | 20 | 25 | 1.49 | S |

152 | 3 | 45 | 20 | 30 | 1.57 | S |

153 | 3 | 45 | 20 | 35 | 1.67 | S |

154 | 3 | 45 | 20 | 40 | 1.76 | S |

155 | 3 | 45 | 20 | 45 | 1.87 | S |

156 | 3 | 45 | 25 | 5 | 1.38 | S |

157 | 3 | 45 | 25 | 10 | 1.49 | S |

158 | 3 | 45 | 25 | 15 | 1.59 | S |

159 | 3 | 45 | 25 | 20 | 1.68 | S |

160 | 3 | 45 | 25 | 25 | 1.76 | S |

161 | 3 | 45 | 25 | 30 | 1.85 | S |

162 | 3 | 45 | 25 | 35 | 1.95 | S |

163 | 3 | 45 | 25 | 40 | 2.06 | S |

164 | 3 | 45 | 25 | 45 | 2.16 | S |

165 | 3 | 45 | 30 | 5 | 1.63 | S |

166 | 3 | 45 | 30 | 10 | 1.75 | S |

167 | 3 | 45 | 30 | 15 | 1.85 | S |

168 | 3 | 45 | 30 | 20 | 1.94 | S |

169 | 3 | 45 | 30 | 25 | 2.04 | S |

170 | 3 | 45 | 30 | 30 | 2.14 | S |

171 | 3 | 45 | 30 | 35 | 2.23 | S |

172 | 3 | 45 | 30 | 40 | 2.34 | S |

173 | 3 | 45 | 30 | 45 | 2.46 | S |

174 | 3 | 45 | 35 | 5 | 1.85 | S |

175 | 3 | 45 | 35 | 10 | 2.01 | S |

176 | 3 | 45 | 35 | 15 | 2.12 | S |

177 | 3 | 45 | 35 | 20 | 2.2 | S |

178 | 3 | 45 | 35 | 25 | 2.31 | S |

179 | 3 | 45 | 35 | 30 | 2.42 | S |

180 | 3 | 45 | 35 | 35 | 2.5 | S |

181 | 3 | 45 | 35 | 40 | 2.61 | S |

182 | 3 | 45 | 35 | 45 | 2.74 | S |

183 | 3 | 45 | 40 | 5 | 2.12 | S |

184 | 3 | 45 | 40 | 10 | 2.25 | S |

185 | 3 | 45 | 40 | 15 | 2.39 | S |

186 | 3 | 45 | 40 | 20 | 2.47 | S |

187 | 3 | 45 | 40 | 25 | 2.58 | S |

188 | 3 | 45 | 40 | 30 | 2.69 | S |

189 | 3 | 45 | 40 | 35 | 2.77 | S |

190 | 3 | 45 | 40 | 40 | 2.91 | S |

191 | 3 | 45 | 40 | 45 | 3.02 | S |

192 | 3 | 45 | 45 | 5 | 2.36 | S |

193 | 3 | 45 | 45 | 10 | 2.5 | S |

194 | 3 | 45 | 45 | 15 | 2.63 | S |

195 | 3 | 45 | 45 | 20 | 2.74 | S |

196 | 3 | 45 | 45 | 25 | 2.85 | S |

197 | 3 | 45 | 45 | 30 | 2.94 | S |

198 | 3 | 45 | 45 | 35 | 3.04 | S |

199 | 3 | 45 | 45 | 40 | 3.18 | S |

200 | 3 | 45 | 45 | 45 | 3.32 | S |

201 | 3 | 45 | 50 | 5 | 2.58 | S |

202 | 3 | 45 | 50 | 10 | 2.74 | S |

203 | 3 | 45 | 50 | 15 | 2.88 | S |

204 | 3 | 45 | 50 | 20 | 2.99 | S |

205 | 3 | 45 | 50 | 25 | 3.1 | S |

206 | 3 | 45 | 50 | 30 | 3.21 | S |

207 | 3 | 45 | 50 | 35 | 3.32 | S |

208 | 3 | 45 | 50 | 40 | 3.46 | S |

209 | 3 | 45 | 50 | 45 | 3.59 | S |

210 | 3 | 45 | 2 | 0 | - | U |

211 | 3 | 45 | 5 | 0 | - | U |

212 | 3 | 45 | 10 | 0 | - | U |

213 | 3 | 45 | 15 | 0 | - | U |

214 | 3 | 45 | 20 | 0 | - | U |

215 | 3 | 45 | 25 | 0 | 1.17 | M |

216 | 3 | 45 | 30 | 0 | 1.4 | S |

217 | 3 | 45 | 35 | 0 | 1.64 | S |

218 | 3 | 45 | 40 | 0 | 1.87 | S |

219 | 3 | 45 | 45 | 0 | 2.13 | S |

220 | 3 | 45 | 50 | 0 | 2.36 | S |

221 | 3 | 63.43 | 2 | 5 | - | U |

222 | 3 | 63.43 | 2 | 10 | - | U |

223 | 3 | 63.43 | 2 | 15 | - | U |

224 | 3 | 63.43 | 2 | 20 | - | U |

225 | 3 | 63.43 | 2 | 25 | - | U |

226 | 3 | 63.43 | 2 | 30 | - | U |

227 | 3 | 63.43 | 2 | 35 | - | U |

228 | 3 | 63.43 | 2 | 40 | - | U |

229 | 3 | 63.43 | 2 | 45 | - | U |

230 | 3 | 63.43 | 5 | 5 | - | U |

231 | 3 | 63.43 | 5 | 10 | - | U |

232 | 3 | 63.43 | 5 | 15 | - | U |

233 | 3 | 63.43 | 5 | 20 | - | U |

234 | 3 | 63.43 | 5 | 25 | - | U |

235 | 3 | 63.43 | 5 | 30 | - | U |

236 | 3 | 63.43 | 5 | 35 | - | U |

237 | 3 | 63.43 | 5 | 40 | - | U |

238 | 3 | 63.43 | 5 | 45 | - | U |

239 | 3 | 63.43 | 10 | 5 | - | U |

240 | 3 | 63.43 | 10 | 10 | - | U |

241 | 3 | 63.43 | 10 | 15 | - | U |

242 | 3 | 63.43 | 10 | 20 | - | U |

243 | 3 | 63.43 | 10 | 25 | - | U |

244 | 3 | 63.43 | 10 | 30 | - | U |

245 | 3 | 63.43 | 10 | 35 | 1.04 | M |

246 | 3 | 63.43 | 10 | 40 | 1.12 | M |

247 | 3 | 63.43 | 10 | 45 | 1.2 | M |

248 | 3 | 63.43 | 15 | 5 | - | U |

249 | 3 | 63.43 | 15 | 10 | - | U |

250 | 3 | 63.43 | 15 | 15 | 1.05 | M |

251 | 3 | 63.43 | 15 | 20 | 1.12 | M |

252 | 3 | 63.43 | 15 | 25 | 1.21 | S |

253 | 3 | 63.43 | 15 | 30 | 1.29 | S |

254 | 3 | 63.43 | 15 | 35 | 1.36 | S |

255 | 3 | 63.43 | 15 | 40 | 1.45 | S |

256 | 3 | 63.43 | 15 | 45 | 1.53 | S |

257 | 3 | 63.43 | 20 | 5 | 1.12 | M |

258 | 3 | 63.43 | 20 | 10 | 1.23 | S |

259 | 3 | 63.43 | 20 | 15 | 1.31 | S |

260 | 3 | 63.43 | 20 | 20 | 1.4 | S |

261 | 3 | 63.43 | 20 | 25 | 1.48 | S |

262 | 3 | 63.43 | 20 | 30 | 1.57 | S |

263 | 3 | 63.43 | 20 | 35 | 1.67 | S |

264 | 3 | 63.43 | 20 | 40 | 1.75 | S |

265 | 3 | 63.43 | 20 | 45 | 1.86 | S |

266 | 3 | 63.43 | 25 | 5 | 1.34 | S |

267 | 3 | 63.43 | 25 | 10 | 1.5 | S |

268 | 3 | 63.43 | 25 | 15 | 1.59 | S |

269 | 3 | 63.43 | 25 | 20 | 1.68 | S |

270 | 3 | 63.43 | 25 | 25 | 1.75 | S |

271 | 3 | 63.43 | 25 | 30 | 1.85 | S |

272 | 3 | 63.43 | 25 | 35 | 1.94 | S |

273 | 3 | 63.43 | 25 | 40 | 2.05 | S |

274 | 3 | 63.43 | 25 | 45 | 2.16 | S |

275 | 3 | 63.43 | 30 | 5 | 1.59 | S |

276 | 3 | 63.43 | 30 | 10 | 1.74 | S |

277 | 3 | 63.43 | 30 | 15 | 1.85 | S |

278 | 3 | 63.43 | 30 | 20 | 1.95 | S |

279 | 3 | 63.43 | 30 | 25 | 2.04 | S |

280 | 3 | 63.43 | 30 | 30 | 2.13 | S |

281 | 3 | 63.43 | 30 | 35 | 2.23 | S |

282 | 3 | 63.43 | 30 | 40 | 2.32 | S |

283 | 3 | 63.43 | 30 | 45 | 2.44 | S |

284 | 3 | 63.43 | 35 | 5 | 1.8 | S |

285 | 3 | 63.43 | 35 | 10 | 1.99 | S |

286 | 3 | 63.43 | 35 | 15 | 2.1 | S |

287 | 3 | 63.43 | 35 | 20 | 2.2 | S |

288 | 3 | 63.43 | 35 | 25 | 2.3 | S |

289 | 3 | 63.43 | 35 | 30 | 2.4 | S |

290 | 3 | 63.43 | 35 | 35 | 2.51 | S |

291 | 3 | 63.43 | 35 | 40 | 2.63 | S |

292 | 3 | 63.43 | 35 | 45 | 2.75 | S |

293 | 3 | 63.43 | 40 | 5 | 2.05 | S |

294 | 3 | 63.43 | 40 | 10 | 2.22 | S |

295 | 3 | 63.43 | 40 | 15 | 2.35 | S |

296 | 3 | 63.43 | 40 | 20 | 2.46 | S |

297 | 3 | 63.43 | 40 | 25 | 2.57 | S |

298 | 3 | 63.43 | 40 | 30 | 2.68 | S |

299 | 3 | 63.43 | 40 | 35 | 2.79 | S |

300 | 3 | 63.43 | 40 | 40 | 2.9 | S |

301 | 3 | 63.43 | 40 | 45 | 3.04 | S |

302 | 3 | 63.43 | 45 | 5 | 2.27 | S |

303 | 3 | 63.43 | 45 | 10 | 2.46 | S |

304 | 3 | 63.43 | 45 | 15 | 2.63 | S |

305 | 3 | 63.43 | 45 | 20 | 2.74 | S |

306 | 3 | 63.43 | 45 | 25 | 2.85 | S |

307 | 3 | 63.43 | 45 | 30 | 2.96 | S |

308 | 3 | 63.43 | 45 | 35 | 3.06 | S |

309 | 3 | 63.43 | 45 | 40 | 3.17 | S |

310 | 3 | 63.43 | 45 | 45 | 3.28 | S |

311 | 3 | 63.43 | 50 | 5 | 2.49 | S |

312 | 3 | 63.43 | 50 | 10 | 2.68 | S |

313 | 3 | 63.43 | 50 | 15 | 2.87 | S |

314 | 3 | 63.43 | 50 | 20 | 3.01 | S |

315 | 3 | 63.43 | 50 | 25 | 3.12 | S |

316 | 3 | 63.43 | 50 | 30 | 3.23 | S |

317 | 3 | 63.43 | 50 | 35 | 3.31 | S |

318 | 3 | 63.43 | 50 | 40 | 3.45 | S |

319 | 3 | 63.43 | 50 | 45 | 3.58 | S |

320 | 3 | 63.43 | 2 | 0 | - | U |

321 | 3 | 63.43 | 5 | 0 | - | U |

322 | 3 | 63.43 | 10 | 0 | - | U |

323 | 3 | 63.43 | 15 | 0 | - | U |

324 | 3 | 63.43 | 20 | 0 | - | U |

325 | 3 | 63.43 | 25 | 0 | 1.15 | M |

326 | 3 | 63.43 | 30 | 0 | 1.36 | S |

327 | 3 | 63.43 | 35 | 0 | 1.56 | S |

328 | 3 | 63.43 | 40 | 0 | 1.81 | S |

329 | 3 | 63.43 | 45 | 0 | 2.05 | S |

330 | 3 | 63.43 | 50 | 0 | 2.27 | S |

331 | 6 | 26.57 | 2 | 5 | - | U |

332 | 6 | 26.57 | 2 | 10 | - | U |

333 | 6 | 26.57 | 2 | 15 | - | U |

334 | 6 | 26.57 | 2 | 20 | - | U |

335 | 6 | 26.57 | 2 | 25 | - | U |

336 | 6 | 26.57 | 2 | 30 | - | U |

337 | 6 | 26.57 | 2 | 35 | - | U |

338 | 6 | 26.57 | 2 | 40 | - | U |

339 | 6 | 26.57 | 2 | 45 | - | U |

340 | 6 | 26.57 | 5 | 5 | - | U |

341 | 6 | 26.57 | 5 | 10 | - | U |

342 | 6 | 26.57 | 5 | 15 | - | U |

343 | 6 | 26.57 | 5 | 20 | - | U |

344 | 6 | 26.57 | 5 | 25 | - | U |

345 | 6 | 26.57 | 5 | 30 | - | U |

346 | 6 | 26.57 | 5 | 35 | - | U |

347 | 6 | 26.57 | 5 | 40 | - | U |

348 | 6 | 26.57 | 5 | 45 | - | U |

349 | 6 | 26.57 | 10 | 5 | - | U |

350 | 6 | 26.57 | 10 | 10 | - | U |

351 | 6 | 26.57 | 10 | 15 | - | U |

352 | 6 | 26.57 | 10 | 20 | - | U |

353 | 6 | 26.57 | 10 | 25 | - | U |

354 | 6 | 26.57 | 10 | 30 | - | U |

355 | 6 | 26.57 | 10 | 35 | 1.05 | M |

356 | 6 | 26.57 | 10 | 40 | 1.11 | M |

357 | 6 | 26.57 | 10 | 45 | 1.2 | M |

358 | 6 | 26.57 | 15 | 5 | - | U |

359 | 6 | 26.57 | 15 | 10 | - | U |

360 | 6 | 26.57 | 15 | 15 | 1.05 | M |

361 | 6 | 26.57 | 15 | 20 | 1.13 | M |

362 | 6 | 26.57 | 15 | 25 | 1.2 | M |

363 | 6 | 26.57 | 15 | 30 | 1.29 | S |

364 | 6 | 26.57 | 15 | 35 | 1.36 | S |

365 | 6 | 26.57 | 15 | 40 | 1.46 | S |

366 | 6 | 26.57 | 15 | 45 | 1.54 | S |

367 | 6 | 26.57 | 20 | 5 | - | U |

368 | 6 | 26.57 | 20 | 10 | 1.21 | S |

369 | 6 | 26.57 | 20 | 15 | 1.32 | S |

370 | 6 | 26.57 | 20 | 20 | 1.4 | S |

371 | 6 | 26.57 | 20 | 25 | 1.49 | S |

372 | 6 | 26.57 | 20 | 30 | 1.57 | S |

373 | 6 | 26.57 | 20 | 35 | 1.66 | S |

374 | 6 | 26.57 | 20 | 40 | 1.75 | S |

375 | 6 | 26.57 | 20 | 45 | 1.86 | S |

376 | 6 | 26.57 | 25 | 5 | 1.18 | M |

377 | 6 | 26.57 | 25 | 10 | 1.43 | S |

378 | 6 | 26.57 | 25 | 15 | 1.58 | S |

379 | 6 | 26.57 | 25 | 20 | 1.67 | S |

380 | 6 | 26.57 | 25 | 25 | 1.76 | S |

381 | 6 | 26.57 | 25 | 30 | 1.85 | S |

382 | 6 | 26.57 | 25 | 35 | 1.94 | S |

383 | 6 | 26.57 | 25 | 40 | 2.05 | S |

384 | 6 | 26.57 | 25 | 45 | 2.17 | S |

385 | 6 | 26.57 | 30 | 5 | 1.36 | S |

386 | 6 | 26.57 | 30 | 10 | 1.64 | S |

387 | 6 | 26.57 | 30 | 15 | 1.81 | S |

388 | 6 | 26.57 | 30 | 20 | 1.87 | S |

389 | 6 | 26.57 | 30 | 25 | 2.01 | S |

390 | 6 | 26.57 | 30 | 30 | 2.13 | S |

391 | 6 | 26.57 | 30 | 35 | 2.23 | S |

392 | 6 | 26.57 | 30 | 40 | 2.34 | S |

393 | 6 | 26.57 | 30 | 45 | 2.46 | S |

394 | 6 | 26.57 | 35 | 5 | 1.51 | S |

395 | 6 | 26.57 | 35 | 10 | 1.81 | S |

396 | 6 | 26.57 | 35 | 15 | 2.03 | S |

397 | 6 | 26.57 | 35 | 20 | 2.2 | S |

398 | 6 | 26.57 | 35 | 25 | 2.27 | S |

399 | 6 | 26.57 | 35 | 30 | 2.41 | S |

400 | 6 | 26.57 | 35 | 35 | 2.5 | S |

401 | 6 | 26.57 | 35 | 40 | 2.62 | S |

402 | 6 | 26.57 | 35 | 45 | 2.74 | S |

403 | 6 | 26.57 | 40 | 5 | 1.73 | S |

404 | 6 | 26.57 | 40 | 10 | 1.99 | S |

405 | 6 | 26.57 | 40 | 15 | 2.24 | S |

406 | 6 | 26.57 | 40 | 20 | 2.45 | S |

407 | 6 | 26.57 | 40 | 25 | 2.55 | S |

408 | 6 | 26.57 | 40 | 30 | 2.68 | S |

409 | 6 | 26.57 | 40 | 35 | 2.77 | S |

410 | 6 | 26.57 | 40 | 40 | 2.9 | S |

411 | 6 | 26.57 | 40 | 45 | 3.02 | S |

412 | 6 | 26.57 | 45 | 5 | 1.88 | S |

413 | 6 | 26.57 | 45 | 10 | 2.18 | S |

414 | 6 | 26.57 | 45 | 15 | 2.45 | S |

415 | 6 | 26.57 | 45 | 20 | 2.68 | S |

416 | 6 | 26.57 | 45 | 25 | 2.84 | S |

417 | 6 | 26.57 | 45 | 30 | 2.95 | S |

418 | 6 | 26.57 | 45 | 35 | 3.06 | S |

419 | 6 | 26.57 | 45 | 40 | 3.17 | S |

420 | 6 | 26.57 | 45 | 45 | 3.31 | S |

421 | 6 | 26.57 | 50 | 5 | 2.09 | S |

422 | 6 | 26.57 | 50 | 10 | 2.36 | S |

423 | 6 | 26.57 | 50 | 15 | 2.63 | S |

424 | 6 | 26.57 | 50 | 20 | 2.86 | S |

425 | 6 | 26.57 | 50 | 25 | 3.08 | S |

426 | 6 | 26.57 | 50 | 30 | 3.15 | S |

427 | 6 | 26.57 | 50 | 35 | 3.27 | S |

428 | 6 | 26.57 | 50 | 40 | 3.45 | S |

429 | 6 | 26.57 | 50 | 45 | 3.58 | S |

430 | 6 | 26.57 | 2 | 0 | - | U |

431 | 6 | 26.57 | 5 | 0 | - | U |

432 | 6 | 26.57 | 10 | 0 | - | U |

433 | 6 | 26.57 | 15 | 0 | - | U |

434 | 6 | 26.57 | 20 | 0 | - | U |

435 | 6 | 26.57 | 25 | 0 | - | U |

436 | 6 | 26.57 | 30 | 0 | 1.08 | M |

437 | 6 | 26.57 | 35 | 0 | 1.27 | S |

438 | 6 | 26.57 | 40 | 0 | 1.44 | S |

439 | 6 | 26.57 | 45 | 0 | 1.6 | S |

440 | 6 | 26.57 | 50 | 0 | 1.81 | S |

441 | 6 | 45 | 2 | 5 | - | U |

442 | 6 | 45 | 2 | 10 | - | U |

443 | 6 | 45 | 2 | 15 | - | U |

444 | 6 | 45 | 2 | 20 | - | U |

445 | 6 | 45 | 2 | 25 | - | U |

446 | 6 | 45 | 2 | 30 | - | U |

447 | 6 | 45 | 2 | 35 | - | U |

448 | 6 | 45 | 2 | 40 | 1 | M |

449 | 6 | 45 | 2 | 45 | 1.15 | M |

450 | 6 | 45 | 5 | 5 | - | U |

451 | 6 | 45 | 5 | 10 | - | U |

452 | 6 | 45 | 5 | 15 | - | U |

453 | 6 | 45 | 5 | 20 | - | U |

454 | 6 | 45 | 5 | 25 | - | U |

455 | 6 | 45 | 5 | 30 | 1.1 | M |

456 | 6 | 45 | 5 | 35 | 1.25 | S |

457 | 6 | 45 | 5 | 40 | 1.39 | S |

458 | 6 | 45 | 5 | 45 | 1.57 | S |

459 | 6 | 45 | 10 | 5 | - | U |

460 | 6 | 45 | 10 | 10 | - | U |

461 | 6 | 45 | 10 | 15 | 1.02 | M |

462 | 6 | 45 | 10 | 20 | 1.17 | M |

463 | 6 | 45 | 10 | 25 | 1.32 | S |

464 | 6 | 45 | 10 | 30 | 1.46 | S |

465 | 6 | 45 | 10 | 35 | 1.63 | S |

466 | 6 | 45 | 10 | 40 | 1.82 | S |

467 | 6 | 45 | 10 | 45 | 2.02 | S |

468 | 6 | 45 | 15 | 5 | - | U |

469 | 6 | 45 | 15 | 10 | 1.15 | M |

470 | 6 | 45 | 15 | 15 | 1.32 | S |

471 | 6 | 45 | 15 | 20 | 1.47 | S |

472 | 6 | 45 | 15 | 25 | 1.63 | S |

473 | 6 | 45 | 15 | 30 | 1.8 | S |

474 | 6 | 45 | 15 | 35 | 1.98 | S |

475 | 6 | 45 | 15 | 40 | 2.17 | S |

476 | 6 | 45 | 15 | 45 | 2.39 | S |

477 | 6 | 45 | 20 | 5 | 1.22 | S |

478 | 6 | 45 | 20 | 10 | 1.42 | S |

479 | 6 | 45 | 20 | 15 | 1.59 | S |

480 | 6 | 45 | 20 | 20 | 1.79 | S |

481 | 6 | 45 | 20 | 25 | 1.93 | S |

482 | 6 | 45 | 20 | 30 | 2.1 | S |

483 | 6 | 45 | 20 | 35 | 2.29 | S |

484 | 6 | 45 | 20 | 40 | 2.51 | S |

485 | 6 | 45 | 20 | 45 | 2.73 | S |

486 | 6 | 45 | 25 | 5 | 1.43 | S |

487 | 6 | 45 | 25 | 10 | 1.68 | S |

488 | 6 | 45 | 25 | 15 | 1.87 | S |

489 | 6 | 45 | 25 | 20 | 2.05 | S |

490 | 6 | 45 | 25 | 25 | 2.23 | S |

491 | 6 | 45 | 25 | 30 | 2.41 | S |

492 | 6 | 45 | 25 | 35 | 2.6 | S |

493 | 6 | 45 | 25 | 40 | 2.81 | S |

494 | 6 | 45 | 25 | 45 | 3.06 | S |

495 | 6 | 45 | 30 | 5 | 1.53 | S |

496 | 6 | 45 | 30 | 10 | 1.94 | S |

497 | 6 | 45 | 30 | 15 | 2.13 | S |

498 | 6 | 45 | 30 | 20 | 2.32 | S |

499 | 6 | 45 | 30 | 25 | 2.52 | S |

500 | 6 | 45 | 30 | 30 | 2.7 | S |

501 | 6 | 45 | 30 | 35 | 2.91 | S |

502 | 6 | 45 | 30 | 40 | 3.12 | S |

503 | 6 | 45 | 30 | 45 | 3.38 | S |

504 | 6 | 45 | 35 | 5 | 1.97 | S |

505 | 6 | 45 | 35 | 10 | 2.19 | S |

506 | 6 | 45 | 35 | 15 | 2.38 | S |

507 | 6 | 45 | 35 | 20 | 2.6 | S |

508 | 6 | 45 | 35 | 25 | 2.79 | S |

509 | 6 | 45 | 35 | 30 | 3.01 | S |

510 | 6 | 45 | 35 | 35 | 3.2 | S |

511 | 6 | 45 | 35 | 40 | 3.42 | S |

512 | 6 | 45 | 35 | 45 | 3.67 | S |

513 | 6 | 45 | 40 | 5 | 2.22 | S |

514 | 6 | 45 | 40 | 10 | 2.44 | S |

515 | 6 | 45 | 40 | 15 | 2.65 | S |

516 | 6 | 45 | 40 | 20 | 2.85 | S |

517 | 6 | 45 | 40 | 25 | 3.08 | S |

518 | 6 | 45 | 40 | 30 | 3.3 | S |

519 | 6 | 45 | 40 | 35 | 3.55 | S |

520 | 6 | 45 | 40 | 40 | 3.7 | S |

521 | 6 | 45 | 40 | 45 | 3.98 | S |

522 | 6 | 45 | 45 | 5 | 2.46 | S |

523 | 6 | 45 | 45 | 10 | 2.71 | S |

524 | 6 | 45 | 45 | 15 | 2.9 | S |

525 | 6 | 45 | 45 | 20 | 3.12 | S |

526 | 6 | 45 | 45 | 25 | 3.32 | S |

527 | 6 | 45 | 45 | 30 | 3.55 | S |

528 | 6 | 45 | 45 | 35 | 3.81 | S |

529 | 6 | 45 | 45 | 40 | 4.04 | S |

530 | 6 | 45 | 45 | 45 | 4.27 | S |

531 | 6 | 45 | 50 | 5 | 2.71 | S |

532 | 6 | 45 | 50 | 10 | 2.95 | S |

533 | 6 | 45 | 50 | 15 | 3.15 | S |

534 | 6 | 45 | 50 | 20 | 3.37 | S |

535 | 6 | 45 | 50 | 25 | 3.58 | S |

536 | 6 | 45 | 50 | 30 | 3.8 | S |

537 | 6 | 45 | 50 | 35 | 4.07 | S |

538 | 6 | 45 | 50 | 40 | 4.33 | S |

539 | 6 | 45 | 50 | 45 | 4.57 | S |

540 | 6 | 45 | 2 | 0 | - | U |

541 | 6 | 45 | 5 | 0 | - | U |

542 | 6 | 45 | 10 | 0 | - | U |

543 | 6 | 45 | 15 | 0 | - | U |

544 | 6 | 45 | 20 | 0 | 1 | M |

545 | 6 | 45 | 25 | 0 | 1.2 | M |

546 | 6 | 45 | 30 | 0 | 1.46 | S |

547 | 6 | 45 | 35 | 0 | 1.61 | S |

548 | 6 | 45 | 40 | 0 | 1.95 | S |

549 | 6 | 45 | 45 | 0 | 2.19 | S |

550 | 6 | 45 | 50 | 0 | 2.37 | S |

551 | 6 | 63.43 | 2 | 5 | - | U |

552 | 6 | 63.43 | 2 | 10 | - | U |

553 | 6 | 63.43 | 2 | 15 | - | U |

554 | 6 | 63.43 | 2 | 20 | - | U |

555 | 6 | 63.43 | 2 | 25 | - | U |

556 | 6 | 63.43 | 2 | 30 | - | U |

557 | 6 | 63.43 | 2 | 35 | - | U |

558 | 6 | 63.43 | 2 | 40 | - | U |

559 | 6 | 63.43 | 2 | 45 | - | U |

560 | 6 | 63.43 | 5 | 5 | - | U |

561 | 6 | 63.43 | 5 | 10 | - | U |

562 | 6 | 63.43 | 5 | 15 | - | U |

563 | 6 | 63.43 | 5 | 20 | - | U |

564 | 6 | 63.43 | 5 | 25 | - | U |

565 | 6 | 63.43 | 5 | 30 | - | U |

566 | 6 | 63.43 | 5 | 35 | - | U |

567 | 6 | 63.43 | 5 | 40 | - | U |

568 | 6 | 63.43 | 5 | 45 | 1.01 | M |

569 | 6 | 63.43 | 10 | 5 | - | U |

570 | 6 | 63.43 | 10 | 10 | - | U |

571 | 6 | 63.43 | 10 | 15 | - | U |

572 | 6 | 63.43 | 10 | 20 | - | U |

573 | 6 | 63.43 | 10 | 25 | - | U |

574 | 6 | 63.43 | 10 | 30 | 1.07 | M |

575 | 6 | 63.43 | 10 | 35 | 1.16 | M |

576 | 6 | 63.43 | 10 | 40 | 1.31 | S |

577 | 6 | 63.43 | 10 | 45 | 1.39 | S |

578 | 6 | 63.43 | 15 | 5 | - | U |

579 | 6 | 63.43 | 15 | 10 | - | U |

580 | 6 | 63.43 | 15 | 15 | 1.04 | M |

581 | 6 | 63.43 | 15 | 20 | 1.14 | M |

582 | 6 | 63.43 | 15 | 25 | 1.25 | S |

583 | 6 | 63.43 | 15 | 30 | 1.35 | S |

584 | 6 | 63.43 | 15 | 35 | 1.46 | S |

585 | 6 | 63.43 | 15 | 40 | 1.59 | S |

586 | 6 | 63.43 | 15 | 45 | 1.75 | S |

587 | 6 | 63.43 | 20 | 5 | 1.03 | M |

588 | 6 | 63.43 | 20 | 10 | 1.16 | M |

589 | 6 | 63.43 | 20 | 15 | 1.29 | S |

590 | 6 | 63.43 | 20 | 20 | 1.41 | S |

591 | 6 | 63.43 | 20 | 25 | 1.52 | S |

592 | 6 | 63.43 | 20 | 30 | 1.63 | S |

593 | 6 | 63.43 | 20 | 35 | 1.74 | S |

594 | 6 | 63.43 | 20 | 40 | 1.87 | S |

595 | 6 | 63.43 | 20 | 45 | 2.02 | S |

596 | 6 | 63.43 | 25 | 5 | 1.26 | S |

597 | 6 | 63.43 | 25 | 10 | 1.39 | S |

598 | 6 | 63.43 | 25 | 15 | 1.53 | S |

599 | 6 | 63.43 | 25 | 20 | 1.65 | S |

600 | 6 | 63.43 | 25 | 25 | 1.77 | S |

601 | 6 | 63.43 | 25 | 30 | 1.9 | S |

602 | 6 | 63.43 | 25 | 35 | 2.02 | S |

603 | 6 | 63.43 | 25 | 40 | 2.15 | S |

604 | 6 | 63.43 | 25 | 45 | 2.28 | S |

605 | 6 | 63.43 | 30 | 5 | 1.48 | S |

606 | 6 | 63.43 | 30 | 10 | 1.63 | S |

607 | 6 | 63.43 | 30 | 15 | 1.75 | S |

608 | 6 | 63.43 | 30 | 20 | 1.88 | S |

609 | 6 | 63.43 | 30 | 25 | 2.01 | S |

610 | 6 | 63.43 | 30 | 30 | 2.15 | S |

611 | 6 | 63.43 | 30 | 35 | 2.29 | S |

612 | 6 | 63.43 | 30 | 40 | 2.42 | S |

613 | 6 | 63.43 | 30 | 45 | 2.55 | S |

614 | 6 | 63.43 | 35 | 5 | 1.71 | S |

615 | 6 | 63.43 | 35 | 10 | 1.84 | S |

616 | 6 | 63.43 | 35 | 15 | 1.99 | S |

617 | 6 | 63.43 | 35 | 20 | 2.12 | S |

618 | 6 | 63.43 | 35 | 25 | 2.25 | S |

619 | 6 | 63.43 | 35 | 30 | 2.39 | S |

620 | 6 | 63.43 | 35 | 35 | 2.54 | S |

621 | 6 | 63.43 | 35 | 40 | 2.69 | S |

622 | 6 | 63.43 | 35 | 45 | 2.84 | S |

623 | 6 | 63.43 | 40 | 5 | 1.92 | S |

624 | 6 | 63.43 | 40 | 10 | 2.08 | S |

625 | 6 | 63.43 | 40 | 15 | 2.21 | S |

626 | 6 | 63.43 | 40 | 20 | 2.35 | S |

627 | 6 | 63.43 | 40 | 25 | 2.49 | S |

628 | 6 | 63.43 | 40 | 30 | 2.63 | S |

629 | 6 | 63.43 | 40 | 35 | 2.78 | S |

630 | 6 | 63.43 | 40 | 40 | 2.94 | S |

631 | 6 | 63.43 | 40 | 45 | 3.13 | S |

632 | 6 | 63.43 | 45 | 5 | 2.14 | S |

633 | 6 | 63.43 | 45 | 10 | 2.29 | S |

634 | 6 | 63.43 | 45 | 15 | 2.43 | S |

635 | 6 | 63.43 | 45 | 20 | 2.57 | S |

636 | 6 | 63.43 | 45 | 25 | 2.71 | S |

637 | 6 | 63.43 | 45 | 30 | 2.86 | S |

638 | 6 | 63.43 | 45 | 35 | 3.02 | S |

639 | 6 | 63.43 | 45 | 40 | 3.18 | S |

640 | 6 | 63.43 | 45 | 45 | 3.37 | S |

641 | 6 | 63.43 | 50 | 5 | 2.36 | S |

642 | 6 | 63.43 | 50 | 10 | 2.53 | S |

643 | 6 | 63.43 | 50 | 15 | 2.67 | S |

644 | 6 | 63.43 | 50 | 20 | 2.81 | S |

645 | 6 | 63.43 | 50 | 25 | 2.95 | S |

646 | 6 | 63.43 | 50 | 30 | 3.09 | S |

647 | 6 | 63.43 | 50 | 35 | 3.25 | S |

648 | 6 | 63.43 | 50 | 40 | 3.44 | S |

649 | 6 | 63.43 | 50 | 45 | 3.61 | S |

650 | 6 | 63.43 | 2 | 0 | - | U |

651 | 6 | 63.43 | 5 | 0 | - | U |

652 | 6 | 63.43 | 10 | 0 | - | U |

653 | 6 | 63.43 | 15 | 0 | - | U |

654 | 6 | 63.43 | 20 | 0 | - | U |

655 | 6 | 63.43 | 25 | 0 | 1.07 | M |

656 | 6 | 63.43 | 30 | 0 | 1.33 | S |

657 | 6 | 63.43 | 35 | 0 | 1.55 | S |

658 | 6 | 63.43 | 40 | 0 | 1.76 | S |

659 | 6 | 63.43 | 45 | 0 | 1.97 | S |

660 | 6 | 63.43 | 50 | 0 | 2.2 | S |

661 | 12 | 45 | 2 | 5 | - | U |

662 | 12 | 45 | 2 | 10 | - | U |

663 | 12 | 45 | 2 | 15 | - | U |

664 | 12 | 45 | 2 | 20 | - | U |

665 | 12 | 45 | 2 | 25 | - | U |

666 | 12 | 45 | 2 | 30 | - | U |

667 | 12 | 45 | 2 | 35 | - | U |

668 | 12 | 45 | 2 | 40 | - | U |

669 | 12 | 45 | 2 | 45 | 1 | M |

670 | 12 | 45 | 5 | 5 | - | U |

671 | 12 | 45 | 5 | 10 | - | U |

672 | 12 | 45 | 5 | 15 | - | U |

673 | 12 | 45 | 5 | 20 | - | U |

674 | 12 | 45 | 5 | 25 | - | U |

675 | 12 | 45 | 5 | 30 | - | U |

676 | 12 | 45 | 5 | 35 | - | U |

677 | 12 | 45 | 5 | 40 | 1.04 | M |

678 | 12 | 45 | 5 | 45 | 1.19 | M |

679 | 12 | 45 | 10 | 5 | - | U |

680 | 12 | 45 | 10 | 10 | - | U |

681 | 12 | 45 | 10 | 15 | - | U |

682 | 12 | 45 | 10 | 20 | - | U |

683 | 12 | 45 | 10 | 25 | - | U |

684 | 12 | 45 | 10 | 30 | 1.06 | M |

685 | 12 | 45 | 10 | 35 | 1.2 | M |

686 | 12 | 45 | 10 | 40 | 1.33 | S |

687 | 12 | 45 | 10 | 45 | 1.47 | S |

688 | 12 | 45 | 15 | 5 | - | U |

689 | 12 | 45 | 15 | 10 | - | U |

690 | 12 | 45 | 15 | 15 | - | U |

691 | 12 | 45 | 15 | 20 | - | U |

692 | 12 | 45 | 15 | 25 | 1.11 | M |

693 | 12 | 45 | 15 | 30 | 1.26 | S |

694 | 12 | 45 | 15 | 35 | 1.4 | S |

695 | 12 | 45 | 15 | 40 | 1.55 | S |

696 | 12 | 45 | 15 | 45 | 1.71 | S |

697 | 12 | 45 | 20 | 5 | - | U |

698 | 12 | 45 | 20 | 10 | - | U |

699 | 12 | 45 | 20 | 15 | 1 | M |

700 | 12 | 45 | 20 | 20 | 1.12 | M |

701 | 12 | 45 | 20 | 25 | 1.29 | S |

702 | 12 | 45 | 20 | 30 | 1.42 | S |

703 | 12 | 45 | 20 | 35 | 1.57 | S |

704 | 12 | 45 | 20 | 40 | 1.76 | S |

705 | 12 | 45 | 20 | 45 | 1.96 | S |

706 | 12 | 45 | 25 | 5 | - | U |

707 | 12 | 45 | 25 | 10 | - | U |

708 | 12 | 45 | 25 | 15 | 1.13 | M |

709 | 12 | 45 | 25 | 20 | 1.26 | S |

710 | 12 | 45 | 25 | 25 | 1.44 | S |

711 | 12 | 45 | 25 | 30 | 1.6 | S |

712 | 12 | 45 | 25 | 35 | 1.75 | S |

713 | 12 | 45 | 25 | 40 | 1.93 | S |

714 | 12 | 45 | 25 | 45 | 2.14 | S |

715 | 12 | 45 | 30 | 5 | - | U |

716 | 12 | 45 | 30 | 10 | 1.07 | M |

717 | 12 | 45 | 30 | 15 | 1.22 | S |

718 | 12 | 45 | 30 | 20 | 1.43 | S |

719 | 12 | 45 | 30 | 25 | 1.55 | S |

720 | 12 | 45 | 30 | 30 | 1.74 | S |

721 | 12 | 45 | 30 | 35 | 1.93 | S |

722 | 12 | 45 | 30 | 40 | 2.11 | S |

723 | 12 | 45 | 30 | 45 | 2.3 | S |

724 | 12 | 45 | 35 | 5 | 1.01 | M |

725 | 12 | 45 | 35 | 10 | 1.19 | M |

726 | 12 | 45 | 35 | 15 | 1.37 | S |

727 | 12 | 45 | 35 | 20 | 1.53 | S |

728 | 12 | 45 | 35 | 25 | 1.67 | S |

729 | 12 | 45 | 35 | 30 | 1.89 | S |

730 | 12 | 45 | 35 | 35 | 2.07 | S |

731 | 12 | 45 | 35 | 40 | 2.27 | S |

732 | 12 | 45 | 35 | 45 | 2.47 | S |

733 | 12 | 45 | 40 | 5 | 1.1 | M |

734 | 12 | 45 | 40 | 10 | 1.29 | S |

735 | 12 | 45 | 40 | 15 | 1.45 | S |

736 | 12 | 45 | 40 | 20 | 1.64 | S |

737 | 12 | 45 | 40 | 25 | 1.86 | S |

738 | 12 | 45 | 40 | 30 | 1.97 | S |

739 | 12 | 45 | 40 | 35 | 2.22 | S |

740 | 12 | 45 | 40 | 40 | 2.44 | S |

741 | 12 | 45 | 40 | 45 | 2.65 | S |

742 | 12 | 45 | 45 | 5 | 1.23 | S |

743 | 12 | 45 | 45 | 10 | 1.42 | S |

744 | 12 | 45 | 45 | 15 | 1.59 | S |

745 | 12 | 45 | 45 | 20 | 1.75 | S |

746 | 12 | 45 | 45 | 25 | 1.97 | S |

747 | 12 | 45 | 45 | 30 | 2.11 | S |

748 | 12 | 45 | 45 | 35 | 2.35 | S |

749 | 12 | 45 | 45 | 40 | 2.57 | S |

750 | 12 | 45 | 45 | 45 | 2.79 | S |

751 | 12 | 45 | 50 | 5 | 1.34 | S |

752 | 12 | 45 | 50 | 10 | 1.53 | S |

753 | 12 | 45 | 50 | 15 | 1.72 | S |

754 | 12 | 45 | 50 | 20 | 1.89 | S |

755 | 12 | 45 | 50 | 25 | 2.07 | S |

756 | 12 | 45 | 50 | 30 | 2.31 | S |

757 | 12 | 45 | 50 | 35 | 2.52 | S |

758 | 12 | 45 | 50 | 40 | 2.73 | S |

759 | 12 | 45 | 50 | 45 | 2.96 | S |

760 | 12 | 45 | 2 | 0 | - | U |

761 | 12 | 45 | 5 | 0 | - | U |

762 | 12 | 45 | 10 | 0 | - | U |

763 | 12 | 45 | 15 | 0 | - | U |

764 | 12 | 45 | 20 | 0 | - | U |

765 | 12 | 45 | 25 | 0 | - | U |

766 | 12 | 45 | 30 | 0 | - | U |

767 | 12 | 45 | 35 | 0 | - | U |

768 | 12 | 45 | 40 | 0 | - | U |

769 | 12 | 45 | 45 | 0 | 1.04 | M |

770 | 12 | 45 | 50 | 0 | 1.15 | M |

771 | 12 | 63.43 | 2 | 5 | - | U |

772 | 12 | 63.43 | 2 | 10 | - | U |

773 | 12 | 63.43 | 2 | 15 | - | U |

774 | 12 | 63.43 | 2 | 20 | - | U |

775 | 12 | 63.43 | 2 | 25 | - | U |

776 | 12 | 63.43 | 2 | 30 | - | U |

777 | 12 | 63.43 | 2 | 35 | - | U |

778 | 12 | 63.43 | 2 | 40 | - | U |

779 | 12 | 63.43 | 2 | 45 | - | U |

780 | 12 | 63.43 | 5 | 5 | - | U |

781 | 12 | 63.43 | 5 | 10 | - | U |

782 | 12 | 63.43 | 5 | 15 | - | U |

783 | 12 | 63.43 | 5 | 20 | - | U |

784 | 12 | 63.43 | 5 | 25 | - | U |

785 | 12 | 63.43 | 5 | 30 | - | U |

786 | 12 | 63.43 | 5 | 35 | - | U |

787 | 12 | 63.43 | 5 | 40 | - | U |

788 | 12 | 63.43 | 5 | 45 | - | U |

789 | 12 | 63.43 | 10 | 5 | - | U |

790 | 12 | 63.43 | 10 | 10 | - | U |

791 | 12 | 63.43 | 10 | 15 | - | U |

792 | 12 | 63.43 | 10 | 20 | - | U |

793 | 12 | 63.43 | 10 | 25 | - | U |

794 | 12 | 63.43 | 10 | 30 | - | U |

795 | 12 | 63.43 | 10 | 35 | - | U |

796 | 12 | 63.43 | 10 | 40 | - | U |

797 | 12 | 63.43 | 10 | 45 | 1.02 | M |

798 | 12 | 63.43 | 15 | 5 | - | U |

799 | 12 | 63.43 | 15 | 10 | - | U |

800 | 12 | 63.43 | 15 | 15 | - | U |

801 | 12 | 63.43 | 15 | 20 | - | U |

802 | 12 | 63.43 | 15 | 25 | - | U |

803 | 12 | 63.43 | 15 | 30 | - | U |

804 | 12 | 63.43 | 15 | 35 | - | U |

805 | 12 | 63.43 | 15 | 40 | 1.04 | M |

806 | 12 | 63.43 | 15 | 45 | 1.14 | M |

807 | 12 | 63.43 | 20 | 5 | - | U |

808 | 12 | 63.43 | 20 | 10 | - | U |

809 | 12 | 63.43 | 20 | 15 | - | U |

810 | 12 | 63.43 | 20 | 20 | - | U |

811 | 12 | 63.43 | 20 | 25 | - | U |

812 | 12 | 63.43 | 20 | 30 | 1.04 | M |

813 | 12 | 63.43 | 20 | 35 | 1.14 | M |

814 | 12 | 63.43 | 20 | 40 | 1.21 | S |

815 | 12 | 63.43 | 20 | 45 | 1.31 | S |

816 | 12 | 63.43 | 25 | 5 | - | U |

817 | 12 | 63.43 | 25 | 10 | - | U |

818 | 12 | 63.43 | 25 | 15 | - | U |

819 | 12 | 63.43 | 25 | 20 | - | U |

820 | 12 | 63.43 | 25 | 25 | 1.09 | M |

821 | 12 | 63.43 | 25 | 30 | 1.19 | M |

822 | 12 | 63.43 | 25 | 35 | 1.3 | S |

823 | 12 | 63.43 | 25 | 40 | 1.39 | S |

824 | 12 | 63.43 | 25 | 45 | 1.48 | S |

825 | 12 | 63.43 | 30 | 5 | - | U |

826 | 12 | 63.43 | 30 | 10 | - | U |

827 | 12 | 63.43 | 30 | 15 | 1.01 | M |

828 | 12 | 63.43 | 30 | 20 | 1.13 | M |

829 | 12 | 63.43 | 30 | 25 | 1.23 | S |

830 | 12 | 63.43 | 30 | 30 | 1.31 | S |

831 | 12 | 63.43 | 30 | 35 | 1.42 | S |

832 | 12 | 63.43 | 30 | 40 | 1.54 | S |

833 | 12 | 63.43 | 30 | 45 | 1.66 | S |

834 | 12 | 63.43 | 35 | 5 | - | U |

835 | 12 | 63.43 | 35 | 10 | 1.03 | M |

836 | 12 | 63.43 | 35 | 15 | 1.14 | M |

837 | 12 | 63.43 | 35 | 20 | 1.27 | S |

838 | 12 | 63.43 | 35 | 25 | 1.35 | S |

839 | 12 | 63.43 | 35 | 30 | 1.46 | S |

840 | 12 | 63.43 | 35 | 35 | 1.56 | S |

841 | 12 | 63.43 | 35 | 40 | 1.62 | S |

842 | 12 | 63.43 | 35 | 45 | 1.81 | S |

843 | 12 | 63.43 | 40 | 5 | 1.01 | M |

844 | 12 | 63.43 | 40 | 10 | 1.13 | M |

845 | 12 | 63.43 | 40 | 15 | 1.29 | S |

846 | 12 | 63.43 | 40 | 20 | 1.24 | S |

847 | 12 | 63.43 | 40 | 25 | 1.49 | S |

848 | 12 | 63.43 | 40 | 30 | 1.58 | S |

849 | 12 | 63.43 | 40 | 35 | 1.7 | S |

850 | 12 | 63.43 | 40 | 40 | 1.81 | S |

851 | 12 | 63.43 | 40 | 45 | 1.97 | S |

852 | 12 | 63.43 | 45 | 5 | 1.13 | M |

853 | 12 | 63.43 | 45 | 10 | 1.25 | S |

854 | 12 | 63.43 | 45 | 15 | 1.4 | S |

855 | 12 | 63.43 | 45 | 20 | 1.51 | S |

856 | 12 | 63.43 | 45 | 25 | 1.64 | S |

857 | 12 | 63.43 | 45 | 30 | 1.73 | S |

858 | 12 | 63.43 | 45 | 35 | 1.83 | S |

859 | 12 | 63.43 | 45 | 40 | 1.96 | S |

860 | 12 | 63.43 | 45 | 45 | 2.07 | S |

861 | 12 | 63.43 | 50 | 5 | 1.21 | S |

862 | 12 | 63.43 | 50 | 10 | 1.31 | S |

863 | 12 | 63.43 | 50 | 15 | 1.49 | S |

864 | 12 | 63.43 | 50 | 20 | 1.64 | S |

865 | 12 | 63.43 | 50 | 25 | 1.7 | S |

866 | 12 | 63.43 | 50 | 30 | 1.83 | S |

867 | 12 | 63.43 | 50 | 35 | 1.97 | S |

868 | 12 | 63.43 | 50 | 40 | 2.06 | S |

869 | 12 | 63.43 | 50 | 45 | 2.24 | S |

870 | 12 | 63.43 | 2 | 0 | - | U |

871 | 12 | 63.43 | 5 | 0 | - | U |

872 | 12 | 63.43 | 10 | 0 | - | U |

873 | 12 | 63.43 | 15 | 0 | - | U |

874 | 12 | 63.43 | 20 | 0 | - | U |

875 | 12 | 63.43 | 25 | 0 | - | U |

876 | 12 | 63.43 | 30 | 0 | - | U |

877 | 12 | 63.43 | 35 | 0 | - | U |

878 | 12 | 63.43 | 40 | 0 | - | U |

879 | 12 | 63.43 | 45 | 0 | - | U |

880 | 12 | 63.43 | 50 | 0 | 1.03 | M |

## References

- Massey, C.; Della Pasqua, F.; Holden, C.; Kaiser, A.; Richards, L.; Wartman, J.; McSaveney, M.J.; Archibald, G.; Yetton, M.; Janku, L. Rock slope response to strong earthquake shaking. Landslides
**2017**, 14, 249–268. [Google Scholar] [CrossRef] - Li, Q.; Wang, Y.M.; Zhang, K.B.; Yu, H.; Tao, Z.Y. Field investigation and numerical study of a siltstone slope instability induced by excavation and rainfall. Landslides
**2020**, 17, 1485–1499. [Google Scholar] [CrossRef] - Nagatani, K.; Abe, M.; Osuka, K.; Chun, P.-j.; Okatani, T.; Nishio, M.; Chikushi, S.; Matsubara, T.; Ikemoto, Y.; Asama, H. Innovative technologies for infrastructure construction and maintenance through collaborative robots based on an open design approach. Adv. Robot.
**2021**, 2021, 715–722. [Google Scholar] [CrossRef] - Das, S.K.; Biswal, R.K.; Sivakugan, N.; Das, B. Classification of slopes and prediction of factor of safety using differential evolution neural networks. Environ. Earth Sci.
**2011**, 64, 201–210. [Google Scholar] [CrossRef] - Sloan, S.W. Geotechnical stability analysis. Geotechnique
**2013**, 63, 531–572. [Google Scholar] [CrossRef] [Green Version] - Cheng, Y.M.; Lansivaara, T.; Wei, W.B. Two-dimensional slope stability analysis by limit equilibrium and strength reduction methods. Comput. Geotech.
**2007**, 34, 137–150. [Google Scholar] [CrossRef] - Reale, C.; Xue, J.; Gavin, K. System reliability of slopes using multimodal optimisation. Geotechnique
**2016**, 66, 413–423. [Google Scholar] [CrossRef] [Green Version] - Tschuchnigg, F.; Schweiger, H.F.; Sloan, S.W.; Lyamin, A.V.; Raissakis, I. Comparison of finite-element limit analysis and strength reduction techniques. Geotechnique
**2015**, 65, 249–257. [Google Scholar] [CrossRef] - Song, D.; Chen, Z.; Chao, H.; Ke, Y.; Nie, W. Numerical study on seismic response of a rock slope with discontinuities based on the time-frequency joint analysis method. Soil Dyn. Earthq. Eng.
**2020**, 133, 106112. [Google Scholar] [CrossRef] - Sakellariou, M.G.; Ferentinou, M.D. A study of slope stability prediction using neural networks. Geotech. Geol. Eng.
**2005**, 23, 419–445. [Google Scholar] [CrossRef] - Samui, P. Slope stability analysis: A support vector machine approach. Environ. Geol.
**2008**, 56, 255–267. [Google Scholar] [CrossRef] - Zhou, J.; Li, E.; Yang, S.; Wang, M.; Shi, X.; Yao, S.; Mitri, H.S. Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf. Sci.
**2019**, 118, 505–518. [Google Scholar] [CrossRef] - Mahmoodzadeh, A.; Mohammadi, M.; Farid Hama Ali, H.; Hashim Ibrahim, H.; Nariman Abdulhamid, S.; Nejati, H.R. Prediction of safety factors for slope stability: Comparison of machine learning techniques. Nat. Hazards
**2022**, 111, 1771–1799. [Google Scholar] [CrossRef] - Shi, N.; Xu, J.; Wurster, S.W.; Guo, H.; Woodring, J.; Van Roekel, L.P.; Shen, H.W. GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations. IEEE Trans. Vis. Comput. Graph.
**2022**, 28, 2301–2313. [Google Scholar] [CrossRef] [PubMed] - Jain, A.K.; Mao, J.; Mohiuddin, K.M. Artificial neural networks: A tutorial. Computer
**1996**, 29, 31–44. [Google Scholar] [CrossRef] [Green Version] - Dreiseitl, S.; Ohno-Machado, L. Logistic regression and artificial neural network classification models: A methodology review. J. Biomed. Inform.
**2002**, 35, 352–359. [Google Scholar] [CrossRef] [Green Version] - Georgevici, A.I.; Terblanche, M. Neural networks and deep learning: A brief introduction. Intensive Care Med.
**2019**, 45, 712–714. [Google Scholar] [CrossRef] [Green Version] - Dahiya, N.; Saini, B.; Chalak, H.D. Deep neural network-based storey drift modelling of precast concrete structures using RStudio. J. Soft Comput. Civ. Eng.
**2021**, 5, 88–100. [Google Scholar] [CrossRef] - Yamane, T.; Chun, P.-J. Crack detection from a concrete surface image based on semantic segmentation using deep learning. J. Adv. Concr. Technol.
**2020**, 18, 493–504. [Google Scholar] [CrossRef] - Chun, P.-j.; Yamane, T.; Tsuzuki, Y. Automatic detection of cracks in asphalt pavement using deep learning to overcome weaknesses in images and gis visualization. Appl. Sci.
**2021**, 11, 892. [Google Scholar] [CrossRef] - Chun, P.-j.; Yamane, T.; Maemura, Y. A deep learning-based image captioning method to automatically generate comprehensive explanations of bridge damage. Comput. Civ. Infrastruct. Eng.
**2022**, 37, 1387–1401. [Google Scholar] [CrossRef] - Yamane, T.; Chun, P.-j.; Dang, J.; Honda, R. Recording of bridge damage areas by 3D integration of multiple images and reduction of the variability in detected results. Comput. Civ. Infrastruct. Eng.
**2023**, 1–17. [Google Scholar] [CrossRef] - Xu, J.J.; Zhang, H.; Tang, C.S.; Cheng, Q.; Liu, B.; Shi, B. Automatic soil desiccation crack recognition using deep learning. Geotechnique
**2022**, 72, 337–349. [Google Scholar] [CrossRef] - Lozano-Diez, A.; Zazo, R.; Toledano, D.T.; Gonzalez-Rodriguez, J. An analysis of the influence of deep neural network (DNN) topology in bottleneck feature based language recognition. PLoS ONE
**2017**, 12, e0182580. [Google Scholar] [CrossRef] [Green Version] - Xu, Y.; Du, J.; Dai, L.R.; Lee, C.H. A regression approach to speech enhancement based on deep neural networks. IEEE/ACM Trans. Audio Speech Lang. Process.
**2015**, 23, 7–19. [Google Scholar] [CrossRef] - Liu, S.Y.; Shao, L.T.; Li, H.J. Slope stability analysis using the limit equilibrium method and two finite element methods. Comput. Geotech.
**2015**, 63, 291–298. [Google Scholar] [CrossRef]

**Figure 1.**A basic model for a simple and homogeneous soil slope with an FOS of 1.640 using numerical simulation.

**Figure 3.**Scatterplots of all input factors: (

**a**) slope height, (

**b**) slope angle, (

**c**) cohesion, and (

**d**) internal friction angle with the obtained FOS.

**Figure 4.**A typical relationship between the FOS and the internal friction angle with slope height of 6 m, slope angle of 45°, and various cohesion values (2–50 kPa).

**Figure 9.**The relationship between the predicted FOS and the “true” FOS using numerical simulation based on the test set.

No. | Cohesion /kPa | Friction Angle /° | FOS * | FOS_LEM [6] | FOS Difference | Relative Error /% |
---|---|---|---|---|---|---|

1 | 2 | 5 | - | 0.25 | - | - |

2 | 2 | 15 | - | 0.50 | - | - |

3 | 2 | 25 | - | 0.74 | - | - |

4 | 2 | 45 | 1.15 | 1.35 | 0.20 | 14.81 |

5 | 5 | 5 | - | 0.41 | - | - |

6 | 5 | 15 | - | 0.70 | - | - |

7 | 5 | 25 | - | 0.98 | - | - |

8 | 5 | 35 | 1.25 | 1.28 | 0.03 | 2.34 |

9 | 5 | 45 | 1.57 | 1.65 | 0.08 | 4.85 |

10 | 10 | 5 | - | 0.65 | - | - |

11 | 10 | 15 | 1.02 | 0.98 | 0.04 | 4.08 |

12 | 10 | 25 | 1.32 | 1.30 | 0.02 | 1.54 |

13 | 10 | 35 | 1.64 | 1.63 | 0.01 | 0.61 |

14 | 10 | 45 | 2.02 | 2.04 | 0.02 | 0.98 |

15 | 20 | 5 | 1.22 | 1.06 | 0.16 | 15.09 |

16 | 20 | 15 | 1.59 | 1.48 | 0.11 | 7.43 |

17 | 20 | 25 | 1.93 | 1.85 | 0.08 | 4.32 |

18 | 20 | 35 | 2.29 | 2.24 | 0.05 | 2.23 |

19 | 20 | 45 | 2.73 | 2.69 | 0.04 | 1.49 |

20 | 5 | 0 | - | 0.20 | - | - |

21 | 10 | 0 | - | 0.40 | - | - |

22 | 20 | 0 | 1.00 | 0.80 | 0.20 | 25.00 |

Output | Training | Cross-Validation Training | Test | |
---|---|---|---|---|

Three classes | Error | 0.0081 | 0.1104 | 0.0778 |

Accuracy | 0.9919 | 0.8896 | 0.9222 | |

Two classes | Error | 0.0308 | 0.0908 | 0.0685 |

Accuracy | 0.9692 | 0.9092 | 0.9315 |

Methods | Numerical Simulation | Classification (Three Classes) | Regression | ||
---|---|---|---|---|---|

Set | - | Training | Test | Training | Test |

Case No. | 1 | 616 (70%) | 264 (30%) | 704 (80%) | 176 (20%) |

Time/s | 125 | 1.549320 | 0.001513 | 0.045756 | 0.003117 |

No. | Cohesion /kPa | Friction Angle /° | FOS_LEM [6] | FOS by Surrogate Model | The Difference | Relative Error /% |
---|---|---|---|---|---|---|

4 | 2 | 45 | 1.35 | 1.16 | −0.19 | −14.37 |

8 | 5 | 35 | 1.28 | 1.24 | −0.04 | −3.41 |

9 | 5 | 45 | 1.65 | 1.54 | −0.11 | −6.72 |

12 | 10 | 25 | 1.30 | 1.33 | 0.03 | 1.97 |

13 | 10 | 35 | 1.63 | 1.63 | 0.00 | 0.04 |

14 | 10 | 45 | 2.04 | 2.02 | −0.02 | −1.05 |

15 | 20 | 5 | 1.06 | 1.16 | 0.10 | 9.58 |

16 | 20 | 15 | 1.48 | 1.60 | 0.12 | 8.11 |

17 | 20 | 25 | 1.85 | 1.93 | 0.08 | 4.56 |

18 | 20 | 35 | 2.24 | 2.29 | 0.05 | 2.40 |

19 | 20 | 45 | 2.69 | 2.75 | 0.06 | 2.21 |

Slope Classification | FOS | Countermeasures |
---|---|---|

U | <1.0 | Prompt treatment |

M | 1.0 ≤ FOS ≤ 1.2 | Monitoring and early warning |

S | >1.2 | Regular monitoring and maintenance |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Li, X.; Nishio, M.; Sugawara, K.; Iwanaga, S.; Chun, P.-j.
Surrogate Model Development for Slope Stability Analysis Using Machine Learning. *Sustainability* **2023**, *15*, 10793.
https://doi.org/10.3390/su151410793

**AMA Style**

Li X, Nishio M, Sugawara K, Iwanaga S, Chun P-j.
Surrogate Model Development for Slope Stability Analysis Using Machine Learning. *Sustainability*. 2023; 15(14):10793.
https://doi.org/10.3390/su151410793

**Chicago/Turabian Style**

Li, Xianfeng, Mayuko Nishio, Kentaro Sugawara, Shoji Iwanaga, and Pang-jo Chun.
2023. "Surrogate Model Development for Slope Stability Analysis Using Machine Learning" *Sustainability* 15, no. 14: 10793.
https://doi.org/10.3390/su151410793