Image processing methodology for detecting delaminations using infrared thermography in CFRP-jacketed concrete members by infrared thermography
Image processing methodology for detecting delaminations using infrared thermography in CFRP-jacketed concrete members by infrared thermography
复制标题
利用红外热成像技术检测 CFRP 夹套混凝土构件分层的图像处理方法
DOI：
10.1016/j.compstruct.2021.114040
复制
发表时间：
2021
影响因子：
6.3
通讯作者：
Shigeki Unjoh
中科院分区：
文献类型：
--
作者：
Jiancheng Gu;Shigeki Unjoh
本研究提出一种利用红外线热成像技术检测碳纤维复合材料（CFRP）夹套混凝土结构分层的方法。在冬季、夏季、晴天和雨天等不同气候条件下，对4个人工脱层试件进行了被动试验。考虑的试验参数为在标本中的人工分层，包括大小，深度，表面覆盖砂浆，和分层空隙中的含水量。该方法检测分层区域的边界识别的基础上在一段时间内的表面温度变化的差异。它可以比基于热图像的视觉评估更有效和准确地检测分层。此外，一些分层，是无法检测到的热图像后，图像处理所提出的方法。此外，结果的准确性受到测试时间和数据收集间隔的显著影响。我们讨论了通过参数分析获得的推荐值，并使用所提出的方法和基于实验数据的深度学习实现了一个应用示例。
This study presents a methodology for detecting delaminations in carbon fiber reinforced polymer (CFRP)-jacketed concrete structures by infrared thermography. Four specimens with artificial delaminations were evaluated through passive experiments under different weather conditions including winter, summer, sunny, and rainy conditions. The test parameters considered for the artificial delaminations in the specimens included size, depth, surface cover mortar, and the water content in the delamination void. The methodology detected delamination regions by boundary recognition based on the differences in surface temperature variations during a period. It could detect delaminations more efficiently and accurately than visual assessments based on thermal images. Furthermore, a few delaminations that were undetectable by thermal images were detected after image processing with the proposed methodology. In addition, the accuracy of the results was significantly affected by the time period for testing and the data-collection intervals. We discuss the recommended values obtained by parametric analysis and implement an application example using the proposed method and deep learning based on the experimental data.
用于混凝土桥梁评估的红外热成像有利时间窗的实际识别
影响因子：
7.4
作者：
Azusa Watase;R. Birgul;Shuhei Hiasa;M. Matsumoto;K. Mitani;F. Catbas
通讯作者：
F. Catbas
影响因子：
6.3
作者：
Gu, Jian-Cheng;Unjoh, Shigeki;Naito, Hideki
通讯作者：
Naito, Hideki