## 6 Conclusions  
In this study, a GA-BP model was presented for the optimization of the design parameters of a reinforced soil embankment with a wrapped face. We conducted analysis using a case study to validate the model's performance. The research conclusions are twofold and are listed below:  
(1) Comparing the GA-BP model with the GBM, RF, DT, GR, and LR algorithms, the ranking scores of the model evaluation indexes of the GA-BP model are 17, 15, 18, 13, and 22, in the relevant order, and in the comparison between the predicted and actual values, the maximum error of the GA-BP model was 8.62%, higher than that of the other algorithms, indicating that GA-BP prediction is more accurate;  
(2) The optimal scheme obtained through GA-BP optimization is compared with the scheme using the training set. The optimized solution provides better control of the evaluation metrics, such as displacement and stress, while ensuring the lowest cost. This comparison validates the feasibility of the GA-BP neural network model in optimizing the design parameters of packaged reinforced soil embankments.  
This study verifies the feasibility of the GA-BP neural network for the optimization of the parameters of a reinforced soil embankment with a wrapped face, but the following shortcomings and outlooks should be considered:  
(1) During the examination of the reinforced soil embankment with a wrapped face established and performed in this paper, only the design parameters of the main part of the embankment are considered, but the foundation and groundwater treatments in actual projects also have a certain influence on the deformation and stability of the embankment. This paper simplifies the foundation part when considering the influencing factors in order to consider the influencing factors of the foundation and groundwater;  
(2) The data set used in the training model in this paper consists of finite element calculation data, and construction monitoring sample data can be collected to train the prediction model in order to accurately predict the deformation and stability of the embankment during the construction process;  
(3) The gradient boosting machine (GBM) algorithm also shows high prediction accuracy when comparing the algorithms, and subsequent research can optimize the GBM algorithm to further compare it with the GA-BP model.  
In practical engineering projects, a more compliant solution is achieved by narrowing the range of values of the design parameters and constraints as needed. Additionally, using more sample data to train the prediction model can provide more accurate and suitable optimization schemes.  
Conceptualization, J.Y. and Y.Q.; methodology, Y.D.; software, Y.Q.; validation, Y.D., J.Y. and Y.Q.; formal analysis, Y.D.; investigation, Y.D.; resources, J.Y.; data curation, Y.D.; writing--original draft preparation, Y.D.; writing--review and editing, J.Y.; visualization, Y.D.; supervision, J.Y.; project administration, J.Y.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Science and Technology Project of Hubei Provincial Department of Transport (2020-2-1-5).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.