# Comparison of Deep Transfer Learning Models for the Quantification of Photoelastic Images
## 4 Conclusions  
In this study, we applied six different deep learning transfer models to quantitatively analyze images obtained from photoelastic experiments on particle models. Among the models tested, the VGG19 model exhibited the most promising results, with the R\({}^{2}\) of 0.988, the MAE of 14.127, and the RMSE of 18.834 for the validation data.  
Previously established quantitative evaluation methods for photoelastic responses, such as [PERSON]'s method and [PERSON]'s method, can predict load levels with high accuracy under specific conditions. However, their accuracy significantly dropped when applied to the reflective photoelastic particle models used in this study. The inherent variability in the attachment quality of the reflective photoelastic material to each particle model is responsible for this discrepancy.  
The deep learning transfer models demonstrated their capability to quantitatively evaluate load levels even under these challenging conditions. All six models--VGG16, VGG19, InceptionV3, ResNet152V2, DenseNet201, and EfficientNetB7--got R\({}^{2}\) values close to 0.9, which means they were better than the old ways of testing photoelastic responses.  
The VGG model outperformed other, more complex transfer learning models in this study. VGG's simplicity in architecture, consisting of 3 \(\times\) 3 convolutional filters and pooling layers, can make it more robust and easier to train on specific datasets. Additionally, when a dataset is similar to pre-trained data, using a pre-trained model for transfer learning is more effective. The photoelastic response images in this study were well suited for training with the VGG model, explaining its superior performance. Similarly, another study used a VGG16-inspired architecture called StressNet for analyzing photoelastic images.  
The exploration of deep learning models in the analysis of photoelastic particle models has shown promising results. Although this study focused on applying learning models to a limited dataset, we anticipate that a more diverse and extensive dataset will enable the development of models that provide even higher accuracy in the future. As geotechnical engineering continues to evolve, the integration of advanced computational methods like deep learning will undoubtedly play a pivotal role in future research and applications.  
Conceptualization, B.H.N. and Y.-H.J.; Data curation, B.H.N.; Formal analysis, Y.-H.J.; Funding acquisition, Y.-H.J.; Investigation, B.H.N.; Methodology, S.K.; Project administration, Y.-H.J.; Resources, Y.-H.J.; Software, S.K.; Supervision, B.H.N. and Y.-H.J.; Validation, S.K., B.H.N. and Y.-H.J.; Visualization, S.K. and B.H.N.; Writing--original draft, S.K.; Writing--review and editing, B.H.N. and Y.-H.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (RS-2023-00254093).  
Not applicable.  
Data Availability Statement: Data available upon request from the authors. The data that support the findings of this study are available from the corresponding author, [PERSON], upon reasonable request.  
The authors declare no conflicts of interest.