Developing Defect Embedded Masonry H-BIM Using Deep Learning-Based Detection and Segmentation

The surface of ancient masonry structures is prone to various defects over time. H-BIM assists in defect inspection digitally, which improves efficiency and saves the labor force. However, existing H-BIM of masonry structures still has limitations of low defect complexity and ideal geometric shape, and the defect information is not integrated with corresponding masonry units, lacking accurate and comprehensive prediction in structural analysis. The developed model presents detailed and realistic masonry units fused with defect information and could be used for numerical simulations. A YOLO model is used to detect and segment defects in masonry structures. K-fold cross-validation is employed during model training to mitigate the impact of category imbalance in the dataset. The YOLO model has also been employed to segment masonry units and extract their contours. The defect information is integrated with masonry units based on their positions. A case study was carried out in an ancient city wall in Suzhou, China. The generated masonry H-BIM assists the current and future protection of the structures, highlighting the feasibility of the method for the analysis of masonry structures.