## 5 Conclusions and Future Research Directions  
In this study, the effects of RDC on the settlement of soft soil embankments were analyzed using field test methods, including ground surface settlement, embankment horizontal displacement, and soil PWP. The monitoring results were compared with data from other research to analyze the difference between the application of RDC in soft soils and coarse-grained soils. The relationship between settlement and other metrics was also investigated. We employed three distinct ML algorithms: RFR, MLP, and XGBoost to predict RDC-induced settlement, and the prediction performances of the three models were then compared in detail. The main conclusions of this study are as follows:  
1. Analysis of the monitoring data revealed that the response of the soil occurred mainly in the first 20 passes. After 20 passes, the settlement of the soil gradually stabilized.
2. The deformation of soil caused by RDC is more significant in soft clay than in coarse-grained materials such as sand and gravel soils under the same conditions.
3. Field monitoring revealed a strong correlation between settlement, horizontal displacement, and pore water pressure. Based on the horizontal displacement and PWP monitoring results, the depth of impact of the RDC on the soft soil embankment was between 3 and 3.5 m.
4. In the prediction of settlement and uneven settlement caused by RDC, the XGBoost and RFR models were found to outperform the MLP model. As a reliable tree-based tool, XGBoost and RFR methods can strike a balance between predictive accuracy and robustness. This study demonstrates the feasibility of using the XGBoost and RFR models for predicting the settlement of soft soil embankments due to RDC and suggests that ensemble learning methods have great potential to predict RDC-induced settlement of complex foundations.  
Conceptualization, D.C., Y.W. and J.L.; methodology, D.C., Y.W. and H.W.; validation, D.C., H.W. and Y.R.; formal analysis, D.C., J.L. and H.W.; investigation, D.C., Y.W. and J.L.; resources, D.C., Y.W. and J.L.; data curation, D.C. and H.W.; writing--original draft preparation, D.C.; writing--review and editing, Y.W.; visualization, D.C. and Y.R.; supervision, Y.W.; project administration, Y.W.; funding acquisition, D.C. and Y.W. All authors have read and agreed to the published version of the manuscript.  
Figure 16: Comparison of predicted results of uneven settlement: (**a**) Training and (**b**) Testing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**R2**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{2-7}  & **Traine** & **Testing** & **Traine** & **Testing** & **Traine** & **Testing** \\ \hline RFR & 0.94 & 0.95 & 0.86 & 0.65 & 2.21 & 0.79 \\ MLP & 0.68 & 0.64 & 3.07 & 2.10 & 5.07 & 3.69 \\ XGBoost & 0.99 & 0.84 & 0.40 & 0.89 & 0.67 & 1.40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance indicators of models in predicting uneven settlement.  
**Funding:** This research was funded by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX21-0493), and the National Natural Science Foundation of China (52378337).  
**Data Availability Statement:** The data presented in this study are available in this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.