图像采集,处理和分析程序的综合框架,用于对混凝土表面的自动损坏评估
Haixu Zhang1, Cassandra Trottier1, Leandro F M Sanchez1
1Department of Civil Engineering, Faculty of Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
Materials (Basel, Switzerland)
|February 24, 2024
概括
本研究引入了基于图像的方法来进行混凝土裂分析,改进了定量损害评估. 人工智能支持的图像分析显示,可靠的混凝土结构健康监测是有前途的.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 混凝土裂的视觉检查是质量和耗时的.
- 人工智能 (AI) 和图像分析为自动混凝土裂检测提供了潜力.
- 可靠的定量分析需要优化图像采集和处理方法.
研究的目的:
- 建立一个标准化的图像采集和处理程序,用于混凝土表面损伤诊断.
- 探索基于图像的测量方法用于定量裂分析的能力.
- 开发和评估一个人工智能模型,以捕捉混凝土中裂的方向性.
主要方法:
- 使用基于图像的测量方法进行混凝土表面裂分析.
- 应用人工智能 (AI) 和机器学习用于自动化裂检测和量化.
- 开发了一个模型来捕捉混凝土裂的异构行为和方向性.
主要成果:
- 发现数字化裂强度测量是可行的,尽管需要更大的数据集.
- 开发的AI模型展示了捕捉裂方向性的能力.
- 在测量和预测的裂方向性值之间观察到统计学上微不足道的差异 (相关系数为0.79和0.82).
结论:
- 基于图像的测量方法对于定量混凝土裂分析是可行的.
- 建议使用更大的数据集进行进一步研究,以提高模型性能.
- 人工智能驱动的图像分析提供了一种可靠的方法来评估具体的结构健康和损伤演变.
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