检测FRP增强混凝土结构的内部缺陷通过红外热学和深度学习的整合
Pengfei Pan1,2, Rongpeng Zhang3, Yi Zhang4
1Xinhua College, Ningxia University, Yinchuan 750021, China.
Materials (Basel, Switzerland)
|July 13, 2024
概括
这项研究使用红外热学和Mask R-CNN深度学习来检测纤维增强聚合物混凝土结构中的隐藏缺陷. 综合方法在识别损伤方面达到96%以上的准确性,增强了结构性健康监测.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
背景情况:
- 结构健康监测 (SHM) 对基础设施至关重要.
- 识别纤维增强聚合物 (FRP) 钢筋混凝土的隐藏缺陷是一项挑战.
- 传统方法往往缺乏精度和自动化.
研究的目的:
- 开发一种用于检测和细分FRP混凝土结构中隐藏缺陷的自动化方法.
- 将红外热学 (IRT) 与深度学习相结合,以提高缺陷检测.
- 为此应用评估Mask R-CNN模型的性能.
主要方法:
- 采用双RGB和热摄像头系统进行数据采集.
- 在捕获的图像数据上执行语义细分注释.
- 使用合并的RGB和热图像训练了一个Mask R-CNN深度学习模型.
- 进行5次交叉验证以评估模型性能.
主要成果:
- 在缺陷检测和细分方面实现了96.28%的平均准确性.
- 证明了高性能指标:特异性 (96.78%),精度 (96.42%) 和回忆 (96.91%).
- 获得了96.78%的F1平均得分,表明有效的损害评估.
结论:
- IRT和深度学习 (Mask R-CNN) 的协同整合为SHM提供了一个强大的工具.
- 这种自动化方法显著提高了检查关键基础设施的精度和效率.
- 开发的方法对于确保FRP混凝土结构的完整性和寿命至关重要.
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