Image analysis is used in this work to quantify cracks in concrete thin sections via modern image processing. Thin sections were impregnated with a yellow epoxy resin, to increase the contrast between voids and other phases of the concrete. By the means of different steps of pre-processing, machine learning and python scripts, cracks can be quantified in an area of up to 40 cm². As a result, the crack area, lengths and widths were estimated automatically within a single workflow. Crack patterns caused by freeze-thaw damages were investigated. To compare the inner degradation of the investigated thin sections, the crack density was used. Cracks in the thin sections were measured manually in two different ways for validation of the automatic determined results. On the one hand, the presented work shows that the width of cracks can be determined pixelwise, thus providing the plot of a width distribution. On the other hand, the automatically measured crack length differs in comparison to the manually measured ones.

中文翻译：

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考虑当前图像分析方法的混凝土薄截面裂缝量化

在这项工作中，图像分析用于通过现代图像处理来量化混凝土薄片中的裂缝。薄切片用黄色环氧树脂浸渍，以增加空隙和混凝土其他相之间的对比度。通过预处理、机器学习和python脚本的不同步骤，可以在最大40 cm ^{2的区域内量化裂缝}. 因此，裂缝面积、长度和宽度在单个工作流程中自动估算。研究了冻融损伤引起的裂纹模式。为了比较所研究的薄片的内部退化，使用了裂纹密度。以两种不同的方式手动测量薄切片中的裂纹，以验证自动确定的结果。一方面，所提出的工作表明裂缝的宽度可以按像素确定，从而提供宽度分布图。另一方面，自动测量的裂纹长度与手动测量的裂纹长度不同。