This research assesses the performance of two deep learning models, SAM and U-Net, for detecting cracks in concrete structures. The results indicate that each model has its own strengths and limitations for detecting different types of cracks. Using the SAM's unique crack detection approach, the image is divided into various parts that identify the location of the crack, making it more effective at detecting longitudinal cracks. On the other hand, the U-Net model can identify positive label pixels to accurately detect the size and location of spalling cracks. By combining both models, more accurate and comprehensive crack detection results can be achieved. The importance of using advanced technologies for crack detection in ensuring the safety and longevity of concrete structures cannot be overstated. This research can have significant implications for civil engineering, as the SAM and U-Net model can be used for a variety of concrete structures, including bridges, buildings, and roads, improving the accuracy and efficiency of crack detection and saving time and resources in maintenance and repair. In conclusion, the SAM and U-Net model presented in this study offer promising solutions for detecting cracks in concrete structures and leveraging the strengths of both models that can lead to more accurate and comprehensive results.

翻译：本研究评估了两种深度学习模型——SAM和U-Net——在混凝土结构裂缝检测中的性能。结果表明，每种模型在检测不同类型的裂缝时各有其优势与局限性。利用SAM独特的裂缝检测方法，图像被分割成多个部分以识别裂缝位置，使其在检测纵向裂缝方面更为有效。另一方面，U-Net模型能够识别正标签像素，准确检测剥落裂缝的尺寸和位置。通过结合两种模型，可以获得更准确、更全面的裂缝检测结果。使用先进技术进行裂缝检测对于确保混凝土结构的安全性和耐久性至关重要。本研究对土木工程具有重要意义，因为SAM和U-Net模型可用于桥梁、建筑物和道路等多种混凝土结构，从而提高裂缝检测的准确性和效率，并在维护和修复中节省时间和资源。总之，本研究提出的SAM和U-Net模型为检测混凝土结构裂缝提供了有前景的解决方案，并利用两种模型的优势能够获得更准确和全面的结果。