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

Kim, Seongmin; Nam, Boo Hyun; Jung, Young-Hoon

doi:10.3390/app14020758

Open AccessArticle

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

by

Seongmin Kim

,

Boo Hyun Nam

^* and

Young-Hoon Jung

^*

Department of Civil Engineering, Kyung Hee University, Yongin 17104, Republic of Korea

^*

Authors to whom correspondence should be addressed.

Appl. Sci. 2024, 14(2), 758; https://doi.org/10.3390/app14020758

Submission received: 22 November 2023 / Revised: 8 January 2024 / Accepted: 10 January 2024 / Published: 16 January 2024

(This article belongs to the Special Issue Advanced Studies in Optical Imaging and Sensing)

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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.

Keywords: reflection photoelastic method; deep learning image analysis; transfer learning model; prediction performance evaluation; granular materials

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MDPI and ACS Style

Kim, S.; Nam, B.H.; Jung, Y.-H. Comparison of Deep Transfer Learning Models for the Quantification of Photoelastic Images. Appl. Sci. 2024, 14, 758. https://doi.org/10.3390/app14020758

AMA Style

Kim S, Nam BH, Jung Y-H. Comparison of Deep Transfer Learning Models for the Quantification of Photoelastic Images. Applied Sciences. 2024; 14(2):758. https://doi.org/10.3390/app14020758

Chicago/Turabian Style

Kim, Seongmin, Boo Hyun Nam, and Young-Hoon Jung. 2024. "Comparison of Deep Transfer Learning Models for the Quantification of Photoelastic Images" Applied Sciences 14, no. 2: 758. https://doi.org/10.3390/app14020758

APA Style

Kim, S., Nam, B. H., & Jung, Y.-H. (2024). Comparison of Deep Transfer Learning Models for the Quantification of Photoelastic Images. Applied Sciences, 14(2), 758. https://doi.org/10.3390/app14020758

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

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