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http://hdl.handle.net/10397/105411
Title: | Automatic generation of structural geometric digital twins from point clouds | Authors: | Mirzaei, K Arashpour, M Asadi, E Masoumi, H Li, H |
Issue Date: | 2022 | Source: | Scientific reports, 2022, v. 12, 22321 | Abstract: | A geometric digital twin (gDT) model capable of leveraging acquired 3D geometric data plays a vital role in digitizing the process of structural health monitoring. This study presents a framework for generating and updating digital twins of existing buildings by inferring semantic information from as-is point clouds (gDT’s data) acquired regularly from laser scanners (gDT’s connection). The information is stored in updatable Building Information Models (BIMs) as gDT’s virtual model, and dimensional outputs are extracted for structural health monitoring (gDT’s service) of different structural members and shapes (gDT’s physical part). First, geometric information, including position and section shape, is obtained from the acquired point cloud using domain-specific contextual knowledge and supervised classification. Then, structural members’ function and section family type is inferred from geometric information. Finally, a BIM is automatically generated or updated as the virtual model of an existing facility and incorporated within the gDT for structural health monitoring. Experiments on real-world construction data are performed to illustrate the efficiency and precision of the proposed model for creating as-is gDT of building structural members. | Publisher: | Nature Publishing Group | Journal: | Scientific reports | EISSN: | 2045-2322 | DOI: | 10.1038/s41598-022-26307-7 | Rights: | © The Author(s) 2022 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The following publication Mirzaei, K., Arashpour, M., Asadi, E. et al. Automatic generation of structural geometric digital twins from point clouds. Sci Rep 12, 22321 (2022) is available at https://doi.org/10.1038/s41598-022-26307-7. |
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