The geometric properties and spatial characteristics of crack patterns are significant indicators of the extent of damage on reinforced concrete structures. However, manual visual assessment is subjective and depends highly on the inspector's skills. The current study proposes an automated approach for the quantification of digitally documented crack patterns on reinforced concrete shell elements subjected to reversed cyclic shear loading. Multifractal analysis is proposed as a feature extractor for images depicting crack patterns and a set of artificial cracks is analyzed, to quantify how the properties of crack patterns vary as a function of cracking inclination. The results of the parametric study motivated the training of a multiclass classification model, which is used to provide damage level estimates for cracked reinforced concrete members. The training of the classifier is performed using experimental data of reinforced concrete shell elements under well‐defined and idealized two‐dimensional pure shear stress loading conditions. A dataset with 119 images from crack patterns of reinforced concrete shells is used for training. The multifractal features successfully translate the shape of the crack patterns into meaningful information about the extent of damage; achieving an overall test accuracy of 89.3%.

中文翻译：

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基于多重分形特征的钢筋混凝土壳体裂纹评估机器学习方法

裂缝模式的几何特性和空间特征是钢筋混凝土结构破坏程度的重要指标。但是，人工视觉评估是主观的，在很大程度上取决于检查员的技能。当前的研究提出了一种自动化的方法，用于量化承受反向循环剪切载荷的钢筋混凝土壳体单元上数字化记录的裂缝模式。提出了多重分形分析作为描述裂纹图案的图像的特征提取器，并分析了一组人工裂纹，以量化裂纹图案的性质如何随裂纹倾斜度而变化。参数研究的结果激发了多类分类模型的训练，用于提供开裂的钢筋混凝土构件的损伤程度估计值。分类器的训练是使用钢筋混凝土壳单元在定义明确且理想的二维纯剪切应力载荷条件下的实验数据进行的。来自钢筋混凝土壳体裂缝模式的119个图像数据集用于训练。多重分形特征成功地将裂纹图案的形状转换为有关损伤程度的有意义的信息。达到89.3％的整体测试精度。多重分形特征成功地将裂纹图案的形状转换为有关损伤程度的有意义的信息。达到89.3％的整体测试精度。多重分形特征成功地将裂纹图案的形状转换为有关损伤程度的有意义的信息。达到89.3％的整体测试精度。