Automatic geometric digital twin of box girder bridge using a laser-scanned point cloud

Geometric modeling is a pivotal step in creating a digital twin for existing bridge structures. Its deficiency of automation makes geometric modeling step time-consuming and laborious. This paper presents a solution for automatically modeling box girder bridges, including external and internal structures, based on laser-scanned point cloud. The solution includes three vital methods: component segmentation, key points extraction of cross-section, and internal structure reconstruction. The results indicate that the established segmentation model, BCR-Net, exhibited better performance than PointNet++ in component segmentation, as demonstrated by mIoU of 0.9751 on the test set. The mean absolute error on the dimension of the pier, bent cap and external and internal structure of the box girder is 0.27 %, 0.38 %, 0.47 %, and 0.48 %, respectively. It means the proposed methods possessed excellent modeling accuracy while ensuring high efficiency than manual modeling, providing a promising solution for digital twin modeling of bridge structures. [Display omitted] •Presented a solution to model box girder bridge including internal structures.•Established the bridge component recognition neural network with favorable performance.•Proposed a strategy selecting cross-sections and a method extracting cross-section key points.•Automatically completed modeling of internal structure based on design drawing.