| Title |
Automated Bridge Component Extraction Method for Enhanced Bridge Scan-to-BIM |
| Authors |
Sung-Jae Bae ; Junbeom Park ; Minji Song ; Joon-Hee Ham ; Jung-Yeol Kim |
| DOI |
https://dx.doi.org/10.6106/KJCEM.2026.27.3.042 |
| Keywords |
Scan-to-BIM; Bridge; Point cloud; Refinement; Clustering |
| Abstract |
The absence or inaccuracy of bridge design drawings hinders effective inspection and maintenance. To overcome this, Scan-to-BIM methods have been studied, yet bridge point cloud data (PCD) often includes various background objects due to outdoor scanning. These irrelevant objects degrade the performance of semantic segmentation and are inefficient to remove manually. Learning-based approaches still struggle with low background recognition accuracy and may exclude critical components. This study proposes a clustering-based method using HDBSCAN, along with a user-interactive software tool that allows efficient background removal while preserving essential structural components. The software supports real-time weight adjustment and visualization for flexible output refinement. A case study on four real-world bridges demonstrated high performance, with precision of 0.90, recall of 0.96, mIoU of 0.87, and overall accuracy of 0.93. The proposed method is practical and well-suited for PCD preprocessing in Scan-to-BIM workflows, providing an effective solution for component extraction with minimal loss of critical data. |