# Comparison of Deep Transfer Learning Models for the Quantification of Photoelastic Images
###### 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. reflection photoelastic method; deep learning image analysis; transfer learning model; prediction performance evaluation; granular materials +
Footnote †: journal: Applied Sciences  
force-chain networks in granular materials [7; 8; 9], how shearing rate affects behavior [10], slip behavior at different packing densities [11], and jamming [12]. Despite the success of photoelasticity, a challenge emerged due to the requirement of using transparent materials for modeling. This limitation arose because the mechanical properties of transparent model grains differed from those of actual mineral grains found in soil particles. [PERSON] [13] introduced the concept of reflective photoelastic coating to address this issue. This technique involves applying a photoelastic material with reflective properties to the model grains, enabling the use of non-transparent materials that better mimic the mechanical behavior of actual soil particles. [PERSON] and others [14] made further advancements by exploring issues related to reinforcing the reflective photoelastic coating method. [PERSON] [15] looked into a lot of different aspects of the reflective technique. He looked into the optical setup using reflection polariscopes, the relationship between stress and strain optics in the context of a reflective photoelastic coating, and the correction factors that are needed to use the method correctly. The researchers discovered that the choice of adhesive for attaching the reflective photoelastic material to the samples significantly influenced the photoelastic response. Furthermore, the wavelength band of the light source and the setup of the measurement camera were crucial considerations that were identified [16]. Recent research has seen progress with the emergence of digital photoelasticity, which enables the observation and analysis of RGB color information using polychromatic light. This replaces conventional monochromatic light and provides additional data for computing fringe orders accurately. [PERSON] et al. [17; 18; 19] observed the photoelastic response using white light and analyzed it in terms of RGB colors. In addition to this, various methods have been employed in digital photoelasticity studies [20; 21; 22]. One notable recent study by [PERSON] and her colleague [23; 24; 25] focused on the contact force network within granular particles. They primarily utilized RGB color data obtained with the reflective photoelastic coating method. Their research sheds light on how the contact force network evolves during loading, providing valuable insights into granular material behavior. However, we noted limitations in quantitatively measuring contact forces for each particle due to fluctuations in the accuracy and precision of the reflective photoelastic coating measurements. This highlights the ongoing challenges and the need for further refinement in this area of research. By employing the photoelastic technique, researchers analyze the changes in photoelastic response in relation to applied load levels and subsequently create calibration data from this analysis. Researchers later use this calibration data as a reference to interpret the photoelastic responses originating from complex problems.