Damage Assessment of RC Beam–Column Joints via Digital Image Correlation and Fractal Dimension Analysis

Beam–column joints are critical elements in reinforced concrete (RC) frame structures. They are subjected to complicated load mechanisms in the event of earthquakes, which often leads to non-ductile damage in the form of shear cracking failure and concrete spalling. With recent advances in computer vision techniques, an image-based methodology is implemented for effectively assessing the seismic damage of RC beam–column joints. Specifically, this study integrates digital image correlation (DIC)-derived strain fields with microplane damage theory to identify concrete surface damage, and subsequently, fractal dimension analysis is employed to quantitatively evaluate the damage metric. The proposed image-based analysis procedures are implemented to investigate the damage evolution of six full-scale RC beam–column joints subjected to quasi-static loading tests. The damage results derived using fractal dimension analysis are compared with those computed using conventional mechanics-based damage models. It is observed that the fractal-based damage index of the RC joints agrees reasonably well with the stiffness-based damage index. The damage curves reveal that the DIC-assisted fractal dimension analysis provides an effective means for automated identification of the surface damage pattern as well as damage progression of RC beam–column joints under seismic loading or other complex loading scenarios.