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基于数字图像相关技术的混凝土结构单轴静态应力估计

Uniaxial Static Stress Estimation for Concrete Structures Using Digital Image Correlation.

机构信息

School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea.

Department of Civil Engineering, University of Seoul, Seoul 02504, Korea.

出版信息

Sensors (Basel). 2019 Jan 15;19(2):319. doi: 10.3390/s19020319.

DOI:10.3390/s19020319
PMID:30650520
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6359448/
Abstract

This paper proposes a static stress estimation method for concrete structures, using the stress relaxation method (SRM) in conjunction with digital image correlation (DIC). The proposed method initially requires a small hole to be drilled in the concrete surface to induce stress relaxation around the hole and, consequently, a displacement field. DIC measures this displacement field by comparing digital images taken before and after the hole-drilling. The stress level in the concrete structure is then determined by solving an optimization problem, designed to minimize the difference between the displacement fields from DIC and the one from a numerical model. Compared to the pointwise measurements by strain gauges, the full-field displacement obtained by DIC provides a larger amount of data, leading to a more accurate estimation. Our theoretical results were experimentally validated using concrete specimens, demonstrating the efficacy of the proposed method.

摘要

本文提出了一种利用应力松弛法(SRM)结合数字图像相关技术(DIC)对混凝土结构进行静态应力估计的方法。该方法首先需要在混凝土表面钻一个小孔,以在孔周围诱导应力松弛,从而产生一个位移场。DIC 通过比较钻孔前后拍摄的数字图像来测量这个位移场。然后,通过求解一个优化问题来确定混凝土结构中的应力水平,该优化问题旨在最小化 DIC 得到的位移场与数值模型得到的位移场之间的差异。与应变片的逐点测量相比,DIC 得到的全场位移提供了更多的数据,从而实现了更准确的估计。我们的理论结果通过混凝土试件进行了实验验证,证明了该方法的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/1cd152a0dc61/sensors-19-00319-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/49caa1ac6b44/sensors-19-00319-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/6e73a52aded6/sensors-19-00319-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/ad74adacbad8/sensors-19-00319-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/2a4a54633ea0/sensors-19-00319-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/5380c624c61d/sensors-19-00319-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/8b3a9a5d459e/sensors-19-00319-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/57f5c3915f82/sensors-19-00319-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/1cd152a0dc61/sensors-19-00319-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/49caa1ac6b44/sensors-19-00319-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/6e73a52aded6/sensors-19-00319-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/ad74adacbad8/sensors-19-00319-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/2a4a54633ea0/sensors-19-00319-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/5380c624c61d/sensors-19-00319-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/8b3a9a5d459e/sensors-19-00319-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/57f5c3915f82/sensors-19-00319-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d2f/6359448/1cd152a0dc61/sensors-19-00319-g008.jpg

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