Comparison of Deep Transfer Learning Models for the Quantification of Photoelastic Images

Featured Application This research has pivotal applications in geotechnical and civil engineering fields, particularly in improving the reliability and precision of stress and strain analysis in granular materials, which can lead to more accurate predictions of soil behavior and further optimize the design and safety of infrastructure.Abstract In the realm of geotechnical engineering, understanding the mechanical behavior of soil particles under external forces is paramount. The main topic of this study is how to use deep learning image analysis techniques, especially transfer learning models like VGG, ResNet, and DenseNet, to look at response images from models of reflective photoelastic soil particles. We applied a total of six transfer learning models to analyze photoelastic response images. We then compared the validation results with existing quantitative evaluation techniques. The researchers identified the most outstanding transfer learning model by comparing the validation results with existing quantitative evaluation techniques using performance metrics such as the coefficient of determination, mean average error, and root mean square error.