High-performance fiber-reinforced cementitious composites (HPFRCCs) feature high mechanical strengths, crack resistance, and durability. Under excessive loading, HPFRCCs demonstrate dense microcracks that are difficult to identify using existing methods. This study presents a computer vision method for identification, quantification, and visualization of microcracks in HPFRCCs based on deep learning. The presented method integrates multiple deep learning models and computer vision techniques in a hierarchical architecture. The crack pattern (e.g., number, width, and spacing of cracks) are automatically determined from pictures without human intervention. This study shows that the presented method achieves an accuracy of 0.992 for crack detection and an accuracy finer than 50 μm (R² > 0.984) for quantification of crack width when deep learning models are trained using only 200 pictures of HPFRCCs and 200 pictures of conventional concrete with incorporation of data augmentation. The presented method is expected to be also applicable to other materials featuring complex cracks.

高性能纤维增强水泥基复合材料 (HPFRCC) 具有高机械强度、抗裂性和耐久性。在过度加载的情况下，HPFRCC 显示出使用现有方法难以识别的致密微裂纹。本研究提出了一种基于深度学习的 HPFRCC 微裂纹识别、量化和可视化的计算机视觉方法。所提出的方法在分层架构中集成了多个深度学习模型和计算机视觉技术。裂纹模式（例如，裂纹的数量、宽度和间距）是根据图片自动确定的，无需人工干预。该研究表明，所提出的方法实现了 0.992 的裂纹检测精度和小于 50 μm (R ² > 0.984) 用于在仅使用 200 张 HPFRCC 图片和 200 张常规混凝土图片并结合数据增强训练深度学习模型时量化裂缝宽度。预计所提出的方法也适用于具有复杂裂纹的其他材料。