Recursive_Segmentation_for_As-Is_Bridge_Informatio.pdf (1.79 MB)

Recursive segmentation for as-is bridge information modelling

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conference contribution
posted on 16.05.2019, 13:05 by Ruodan Lu, Ioannis Brilakis
Prior studies reported that the time needed to manually convert a point cloud to an as-is geometric model using cutting edge modelling software is ten times greater than the time needed to obtain the point cloud. The laborious nature of manually modelling infrastructure such as bridges is the reason behind the significant cost of modelling which impedes the proliferation of the usage of Bridge Information Models (BrIM) in Bridge Management Systems. Existing commercial solutions can automatically recognize geometric shapes embedded in segmented point cloud data (PCD) and generate the corresponding IFC objects. Researchers have taken further studies and have additionally automated surface reconstruction through generating parametric surface-based primitives in order to automate the segmentation process. However, surface-based segmentation for bridge modelling is an unsolved problem, which is neither straightforward nor consistent, thus hinders the automation of BrIM.This paper presents a top-down PCD detection solution that follows a knowledge-based heuristic approach for BrIM generation that can semi-automatically segment a bridge point cloud recursively. We leverage bridge domain knowledge as strong priors through a histogram-based algorithm to conduct the tasks of segmentation and classification. We implemented this solution and tested on one highway bridge. The experimental results indicated that the detection precision of this solution is 92%.

Funding

EPSRC and Infravation SeeBridge project.

History

School

  • Architecture, Building and Civil Engineering

Published in

Lean and Computing in Construction Congress - Joint Conference on Computing in Construction Lean and Computing in Construction Congress - Volume 1: Proceedings of the Joint Conference on Computing in Construction

Citation

LU, R. and BRILAKIS, I., 2017. Recursive segmentation for as-is bridge information modelling. IN: Bosche, F., Brilakis, I. and Sacks, R. (eds). LC3 2017: Volume 1 - Proceedings of the Joint Conference on Computing in Construction (JC3), Heraklion, Crete, Greece, 4-7 July 2017, pp.209-217.

Publisher

Heriot-Watt University © Frédéric Bosché, Editor

Version

VoR (Version of Record)

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/

Publication date

2017

Notes

This is a conference paper. It first appeared in LU, R. and BRILAKIS, I., 2017. Recursive segmentation for as-is bridge information modelling. IN: Bosche, F., Brilakis, I. and Sacks, R. (eds). LC3 2017: Volume 1 - Proceedings of the Joint Conference on Computing in Construction (JC3), Heraklion, Crete, Greece, 4-7 July 2017, pp.209-217.

ISBN

9780956595164

Language

en

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