Semi-automated localised updating for as-built BIM of piping systems using point cloud data

As-designed building information models (BIM) often diverge from as-built conditions, limiting their reliability during the operation and maintenance (O&M). Current research focuses on change detection but lacks a systematic workflow for reliable updates, especially for piping systems with frequent changes and complex geometries. The paper addresses how to establish a semi-automated, end-to-end workflow for localised updating as-designed BIM of piping systems from point cloud data. The workflow applies PointNet++ for segmentation, followed by iterative closest point, random sample consensus, and region-growing for geometry extraction. The proposed BIM updating taxonomy and dedicated pre-judgment updating requirements (PUR) and spatial and topological relationships up-dating (STRU) algorithms identify update requirements and automate parametric updates. Validation through case studies demonstrates the workflow's ability to accurately perform localised updates, reducing the manual workload by approximately 70 %. This practical, scalable solution strengthens O&M by maintaining accurate as-built models and inspires future automated BIM updating research.