Automated and Robust Virtual Trial Assembly of Prefabricated Bridge Based on Point Cloud Data

Virtual trial assembly (VTA), an alternative to physical trial assembly, has been used in many projects owing to its advantages of low time consumption and low cost. However, the existing methods based on the terrestrial laser scanner struggle with noise, incomplete boundaries, and arbitrary initial poses, making them impractical for steel members of long-span suspension bridges with bolted connections. Therefore, this study proposes a novel method combining robust circle fitting and matching constrained based on multiple features. Firstly, alpha shape and DBSCAN algorithms precisely extract hole boundaries from unstructured point clouds. Secondly, a Huber loss model optimized via Iteratively Reweighted Least Squares (IRLS) extracts hole centers, effectively overcoming measurement noise and local defects. Thirdly, integrating global polar coordinates and local topological descriptors establishes reliable correspondences without prior pose information. Finally, singular value decomposition calculates the optimal rigid transformation matrix for spatial alignment. The results show the novel method consistently outperforms common Least Squares (LS), Pratt, Taubin, and Random Sample Consensus (RANSAC) algorithms regarding feature extraction stability. Evaluated exclusively upon the specific point cloud dataset acquired in this study, the proposed algorithm achieved a minimal internal registration Root Mean Square Error (RMSE) of 0.148 mm and a Mean Absolute Error (MAE) of 0.076 mm. These specific algorithmic residuals confirm highly reliable relative computational consistency for the tested bridge components, though extensive validations remain necessary for generalized field conditions.