# Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay
## 5 Conclusions  
In this study, an application of ANN to predict the cantilever wall deflection in undrained clay based on a set of numerical data from verified finite element models was presented. The proposed ANN model was well trained, tested, and validated, indicating its reliable performance. In addition, a series of parametric studies were conducted to  
Figure 17: ANN interactive graphical user interface.  
evaluate the influences of six input parameters on the cantilever wall deflection. Based on the analysis, the following conclusions were drawn:  
* The proposed ANN model can predict the cantilever wall deflection in undrained clay with acceptable performance.
* The most influential parameter in determining the cantilever wall deflection in undrained clay is the excavation depth, followed by the soil stiffness ratio at mid-depth of the wall, while the at-rest lateral earth pressure coefficient presents least influence among the considered parameters.
* An ANN model-based formula, which included all input parameters, was developed to calculate the cantilever wall deflection.
* A new GUI was developed to easily predict the cantilever wall deflection in undrained clay. The developed GUI tool is much simpler and more cost-efficient and can be recommended for use in practical engineering in advance.  
However, there are some limitations to the proposed ANN model. First, it should be noted that the database in this study is generally small, and more high-quality datasets urgently need to be generated for a future investigation. Second, commonly used K-fold cross-validation methods can be used to improve the performance and robustness of the proposed ANN model. Third, comparisons should be made between the proposed ANN model and other mathematical models to verify the performance and reliability of the proposed ANN model. Finally, to enhance the ability and robustness of the proposed ANN model in more complicate problems, extra work on the use of other strong machine learning algorithms, such as the modular neural network and radial basis function neural network, could be performed.  
Z.H.: conceptualization, methodology, writing (original draft), data curation; D.Z. ([PERSON]): funding acquisition, project administration, supervision; D.Z. ([PERSON]): methodology and data curation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grants No. 52108381, 52090082, 41772295, 51978517), the Innovation Program of Shanghai Municipal Education Commission (Grant No. 2019-01-07-00-07-456 E00051), the Shanghai Science and Technology Committee Program (No. 20 dz1201404, 21 DZ1200601), and the key innovation team program of innovation talents promotion plan by MOST of China (No. 2016 RA4059).  
Not applicable.  
All data generated or analyzed during this study are included in this published article.  
The author would like to thank the editors for their helpful and constructive comments that greatly contributed to improving the quality of the paper. The helpful comments from three anonymous reviewers are also greatly appreciated by the authors.