Knowledge-based registration for reliable correlated change detection on high-pier curved continuous rigid frame bridge

Change detection on continuous rigid frame bridges is challenging due to its complex shapes. Accurate and effective change analysis is necessary to not only locate the changes but also investigate the root causes behind. Registration of 3D imageries collected at different times for structural change analysis, however, is an obstacle even with the help of advanced registration algorithms. This study proposed a knowledge-based registration approach that segments point clouds into structural elements for recovering the correlated torsional and bending behaviors of connected bridge elements. The proposed approach uses the joint equilibrium condition between structural elements as domain knowledge to guide the selection of correspondences for point cloud registration and element-level change detection. Besides, the approach iterates the correspondence locating process until change detection results comply with the joint equilibrium condition. The results indicate that the proposed knowledge-based registration approach provides more reliable correlated change detection results than using the Iterative Closest Point (ICP) algorithms.