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Topological Relationship Modelling for Industrial Facility Digitisation Using Graph Neural Networks

  • Haritha Jayasinghe
  • Ioannis Brilakis

There is rising demand for automated digital twin construction based on point cloud scans, especially in the domain of industrial facilities. Yet, current automation approaches focus almost exclusively on geometric modelling. The output of these methods is a disjoint cluster of individual elements, while element relationships are ignored. This research demonstrates the feasibility of adopting Graph Neural Networks (GNN) for automated detection of connectivity relationships between elements in industrial facility scans. We propose a novel method which represents elements and relationships as graph nodes and edges respectively. Element geometry is encoded into graph node features. This allows relationship inference to be modelled as a graph link prediction task. We thereby demonstrate that connectivity relationships can be learned from existing design files, without requiring domain specific, hand-coded rules, or manual annotations. Preliminary results show that our method performs successfully on a synthetic point cloud testset generated from design files with a 0.64 F1 score. We further demonstrate that the method adapts to occluded real-world scans. The method can be further extended with the introduction of more descriptive node features. Additionally, we present tools for relationship annotation and visualisation to aid relationship detection

  • Keywords:
  • BIM,
  • Digital twin,
  • GNN,
  • machine learning,
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Haritha Jayasinghe

University of Cambridge, United Kingdom - ORCID: 0000-0002-6236-8092

Ioannis Brilakis

University of Cambridge, United Kingdom - ORCID: 0000-0003-1829-2083

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  • Publication Year: 2023
  • Pages: 887-894

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  • Publication Year: 2023

Chapter Information

Chapter Title

Topological Relationship Modelling for Industrial Facility Digitisation Using Graph Neural Networks

Authors

Haritha Jayasinghe, Ioannis Brilakis

DOI

10.36253/979-12-215-0289-3.88

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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