# Settlement Prediction and Differential Settlement Criterion for Heightening and Thickening Leveee
## 3 Case Study
### Case Introduction
#### 3.2.2 Application for Specific Section of HTL  
Ground Settlement Prediction  
The ground settlement rules are given in Figure 11. The following conclusions can be drawn from Figure 11: 1. The settlement rules of different points show different shapes (S-type and hyperbolic); 2. The measured points at the foot of the slope show hyperbolic settlement, while the measured points on the top of the slope show S-type settlement; 3. The settlement stability time at the foot of the slope is shorter than that at the top of the slope; 4. The settlement of the top of the slope is larger than that at \begin{table}
\begin{tabular}{c c c} \hline
**Monitor Points** & **Prediction Models** & **Sum of Squares of Errors** \\ \hline \multirow{3}{*}{Section I} & GSPM & 25.1 \\  & Logistic model & 35.5 \\  & Hyperbolic model & 27.9 \\ \hline \multirow{3}{*}{Section II} & GSPM & 53.7 \\  & Logistic model & 55.5 \\ \cline{1-1}  & Hyperbolic model & 99.1 \\ \hline \end{tabular}
\end{table} 
Table 6: Sum of squares of errors for different sections unit:mm\({}^{2}\). Figure 10: Verification for different sections of HTL. the foot of the slope. 5. The GSPM is applicable to settlement prediction, which is more stable than the hyperbolic model and logistic model. Because of stress diffusion, the additional stress at the foot of the HTL is less than that at the top of the HTL, which explains why the deformation at the corner of the HTL is less than that at the top of the HTL. Because the HTL is located in a seasonal freezing and thawing area, the temperature in winter can reach \(-30\)\({}^{\circ}\)C. Under winter rainfall, soil at the top of the HTL is easily frozen, and the settlement of the HTL decreases with the increase of strength. After the ice and snow melts, the soil particles are rearranged, resulting in thaw settlement [37; 38] and accelerated settlement. Therefore, the role of frozen soil in a seasonal freezing and thawing area can explain the S settlement rules at the top of the HTL. For monitoring points G1 and A, the settlement-time curve is hyperbolic, as seen from Figure 11. From monitoring point A, the logistic model has the same problem for predicting the settlement stabilization time. For the final settlement prediction, the results from the logistic model are less accurate than those from the other two models. There are three conclusions based on monitoring points G2, B, and L1: 1. The hyperbolic model is not suitable for predicting, which is consistent with Section 3.2.1; 2. Both the logistic model and  
Figure 11: Settlement–time curve for ground settlement. the GSPM are suitable for predicting an S-shaped settlement-time curve; 3. For small settlements (0-3 mm), the logistic model can reduce the settlement stabilization time. The GSPM can be more widely used, more accurate, and more stable than the traditional logistic model and hyperbolic model. Internal Settlement Prediction  
The rules of internal settlement of the HTL are given in Figure 12. The following conclusions can be drawn from Figure 12: 1. Settlements within 1 m of the top of the HTL tend to be S-shaped, while those below 4 m of the depth of the HTL tend to be hyperbolic; 2. The GSPM is also applicable in describing the law of internal settlement of the HTL. Moreover, the GSPM is more stable than the logistic model and hyperbolic model; 3. The internal settlement of the HTL is more than that of the top. Similarly, owing to the influence of frozen soil at surface, the settlement of the soil near the surface appears S-shaped. Because the residents around the monitoring section have the habit of pumping groundwater, the descent of groundwater will cause internal settlement [6; 39; 40]. Table 7 lists the sum of squares of errors of different models. The bold words in Table 7 represent the sum of squares of errors of GSPM, and it is also the minimum error among the three models, which shows that GSPM is more stable and adaptable than the logistic model and hyperbolic model. That's because the GSPM has more parameters. Hence, the new model needs more data to fit.