Chapter Topological Relationship Modelling for Industrial Facility Digitisation Using Graph Neural Networks
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,...
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| Materialtyp: | Online |
| Språk: | engelska |
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Firenze University Press
2024
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| Länkar: | ONIX_20240402_9791221502893_13 |
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| _version_ | 1869515110531203072 |
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| author | Jayasinghe, Haritha Brilakis, Ioannis |
| author_browse | Brilakis, Ioannis Jayasinghe, Haritha |
| author_facet | Jayasinghe, Haritha Brilakis, Ioannis |
| author_sort | Jayasinghe, Haritha |
| collection | Directory of Open Access Books |
| description | 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 |
| format | Online |
| id | doab-20.500.12854ir-136916 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1369162024-05-10T18:13:54Z Chapter Topological Relationship Modelling for Industrial Facility Digitisation Using Graph Neural Networks Jayasinghe, Haritha Brilakis, Ioannis BIM Digital twin GNN machine learning thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization 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 2024-05-10T18:13:51Z 2024-05-10T18:13:51Z 2024-04-02T15:44:40Z 2023 chapter ONIX_20240402_9791221502893_13 2704-5846 https://library.oapen.org/handle/20.500.12657/89044 9791221502893 https://directory.doabooks.org/handle/20.500.12854/136916 eng Proceedings e report open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/89044/1/9791221502893_88.pdf Firenze University Press 10.36253/979-12-215-0289-3.88 10.36253/979-12-215-0289-3.88 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9791221502893 8 Florence open access |
| spellingShingle | BIM Digital twin GNN machine learning thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization Jayasinghe, Haritha Brilakis, Ioannis Chapter Topological Relationship Modelling for Industrial Facility Digitisation Using Graph Neural Networks |
| title | Chapter Topological Relationship Modelling for Industrial Facility Digitisation Using Graph Neural Networks |
| title_full | Chapter Topological Relationship Modelling for Industrial Facility Digitisation Using Graph Neural Networks |
| title_fullStr | Chapter Topological Relationship Modelling for Industrial Facility Digitisation Using Graph Neural Networks |
| title_full_unstemmed | Chapter Topological Relationship Modelling for Industrial Facility Digitisation Using Graph Neural Networks |
| title_short | Chapter Topological Relationship Modelling for Industrial Facility Digitisation Using Graph Neural Networks |
| title_sort | chapter topological relationship modelling for industrial facility digitisation using graph neural networks |
| topic | BIM Digital twin GNN machine learning thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization |
| topic_facet | BIM Digital twin GNN machine learning thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization |
| url | ONIX_20240402_9791221502893_13 |
| work_keys_str_mv | AT jayasingheharitha chaptertopologicalrelationshipmodellingforindustrialfacilitydigitisationusinggraphneuralnetworks AT brilakisioannis chaptertopologicalrelationshipmodellingforindustrialfacilitydigitisationusinggraphneuralnetworks |