Scan-to-BIM-to-Sim: Automated reconstruction of digital and simulation models from point clouds with applications on bridges
The automation of 3D geometric model reconstruction from point clouds is essential for efficient management of critical infrastructure assets like bridges, significantly streamlining and enhancing inspection and structural analysis tasks. However, existing automated frameworks frequently encounter c...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025003743 |
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| Summary: | The automation of 3D geometric model reconstruction from point clouds is essential for efficient management of critical infrastructure assets like bridges, significantly streamlining and enhancing inspection and structural analysis tasks. However, existing automated frameworks frequently encounter challenges due to substantial computational demands and the difficulties when applied to defective point clouds, which arise from adverse environmental conditions, measurement errors, and limitations of surveying equipment. Major limitations also exist in achieving real-time data exchange and mapping between Building Information Modelling (BIM) models and simulation (Sim) models. To address this gap, this paper proposes a comprehensive Scan-to-BIM-to-Sim framework that efficiently reconstructs bridge BIM models from imperfect point clouds and supports bidirectional mapping between BIM and numerical simulations. The method proposes an improved edge detection for contour extraction from imperfect point cloud and parametric modelling for the direct generation of 3D models within BIM software. Additionally, it automates the exchange between BIM models and simulation software, facilitating bidirectional operation for real-time analysis and visualisation. The framework, validated using the Arial Aqueduct Bridge case, reduces modelling time and computational demands, thereby streamlining construction simulations with improved accuracy and cost efficiency. The dataset collected for model generation and validation is openly available at https://doi.org/10.17632/znxxsgn2ky.1. |
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| ISSN: | 2590-1230 |