光弹性
压力(语言学)
领域(数学)
生成语法
相似性(几何)
应力场
生成对抗网络
计算机科学
对抗制
理论(学习稳定性)
人工智能
深度学习
图像(数学)
机器学习
工程类
数学
结构工程
数学分析
语言学
有限元法
哲学
柯西应力张量
纯数学
作者
Juan Carlos Briñez de León,Rubén D. Fonnegra,Alejandro Restrepo-Martínez
摘要
For overcoming conventional photoelasticity limitations when evaluating the stress field in loaded bodies, this paper proposes a Generative Adversarial Network (GAN) while maintaining performance, gaining experimental stability, and shorting time response. Due to the absence of public photoelasticity data, a synthetic dataset was generated by using analytic stress maps and crops from them. In this case, more than 100000 pair of images relating fringe colors to their respective stress surfaces were used for learning to unwrap the stress information contained into the fringes. Main results of the model indicate its capability of recovering the stress field achieving an averaged performance of 0.93±0.18 according to the structural similarity index (SSIM). These results represent a great opportunity for exploring GAN models in real time stress evaluations.
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