LuBrilakis_EC3_2019_v4.pdf (727.03 kB)
Generating bridge geometric digital twins from point clouds
conference contribution
posted on 2019-05-17, 09:15 authored by Ruodan Lu, Ioannis BrilakisThe automation of digital twinning for existing bridges from point clouds remains unsolved. Extensive manual effort is required to extract object point clusters from point clouds followed by fitting them with accurate 3D shapes. Previous research yielded methods that can automatically generate surface primitives combined
with rule-based classification to create labelled cuboids and cylinders. While these methods work well in synthetic datasets or simplified cases, they encounter huge challenges when dealing with realworld point clouds. In addition, bridge geometries,
defined with curved alignments and varying
elevations, are much more complicated than idealized cases. None of the existing methods can handle these difficulties reliably. The proposed framework employs
bridge engineering knowledge that mimics the
intelligence of human modellers to detect and model reinforced concrete bridge objects in imperfect point clouds. It directly produces labelled 3D objects in Industry Foundation Classes format without
generating low-level shape primitives. Experiments on ten bridge point clouds indicate the framework achieves an overall detection F1-score of 98.4%, an average modelling accuracy of 7.05 cm, and an
average modelling time of merely 37.8 seconds. This is the first framework of its kind to achieve high and reliable performance of geometric digital twin
generation of existing bridges.
Funding
EPSRC, EU Infravation SeeBridge project under Grant No. 31109806.0007 and Trimble Research Fund.
History
School
- Architecture, Building and Civil Engineering
Published in
2019 European Conference on Computing in ConstructionCitation
LU, R. and BRILAKIS, I., 2019. Generating bridge geometric digital twins from point clouds. IN: O’Donnell, J. ... et al (eds). Proceedings of the 2019 Conference of European Council on Computing in Construction (EC3), Chania, Crete, Greece, 10-12 July 2019, pp.367-376.Publisher
European Council on Computing in Construction © The AuthorsVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2019-04-15Publication date
2019Notes
This is a conference paper.ISBN
9781910963371ISSN
1234-5678Publisher version
Language
- en