- CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
- A cura di Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Topological Relationship Modelling for Industrial Facility Digitisation Using Graph Neural Networks
- Haritha Jayasinghe
- Ioannis Brilakis
- © 2023 Author(s) |
- CC BY-NC 4.0
- DOI: 10.36253/979-12-215-0289-3.88
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,
University of Cambridge, United Kingdom - ORCID: 0000-0002-6236-8092
University of Cambridge, United Kingdom - ORCID: 0000-0003-1829-2083
- Agapaki, E., S. M., Glyn-Davies, A., Mandoki, S., Brilakis, I., M., Candidate, P. D., O’, L., & Reader, R. (2019). CLOI: A Shape Classification Benchmark Dataset for Industrial Facilities. DOI: 10.1061/9780784482445.009
- Agapaki, E., & Brilakis, I. (2022). Geometric Digital Twinning of Industrial Facilities: Retrieval of Industrial Shapes. DOI: 10.48550/arxiv.2202.04834
- Agapaki, E., Miatt, G., & Brilakis, I. (2018). Prioritizing object types for modelling existing industrial facilities. Automation in Construction, 96, 211–223. DOI: 10.1016/j.autcon.2018.09.011
- Bloch, T., & Sacks, R. (2020). Clustering Information Types for Semantic Enrichment of Building Information Models to Support Automated Code Compliance Checking. Journal of Computing in Civil Engineering, 34(6), 04020040. DOI: 10.1061/(asce)cp.1943-5487.0000922
- Buruzs, A., Šipetić, M., Blank-Landeshammer, B., & Zucker, G. (2022). IFC BIM Model Enrichment with Space Function Information Using Graph Neural Networks. Energies, 15(8). DOI: 10.3390/en15082937
- Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Inductive Representation Learning on Large Graphs. http://arxiv.org/abs/1706.02216
- Ismail, A., Strug, B., & Ślusarczyk, G. (2018). Building Knowledge Extraction from BIM/IFC Data for Analysis in Graph Databases (pp. 652–664). DOI: 10.1007/978-3-319-91262-2_57
- Kipf, T. N., & Welling, M. (2016). Semi-Supervised Classification with Graph Convolutional Networks. 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings. DOI: 10.48550/arxiv.1609.02907
- Nguyen, T. H., Oloufa, A. A., & Nassar, K. (2005). Algorithms for automated deduction of topological information. Automation in Construction, 14(1), 59–70. DOI: 10.1016/J.AUTCON.2004.07.015
- Oh, I., & Kwang, H. (2021). Automated recognition of 3D pipelines from point clouds. The Visual Computer, 37, 1385–1400. DOI: 10.1007/s00371-020-01872-y
- Qi, C. R., Su, H., Mo, K., & Guibas, L. J. (2016). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. http://arxiv.org/abs/1612.00593
- Sager, C., Zschech, P., & Erlangen-Nürnberg Niklas Kühl, F.-A.-U. (2021). labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds. DOI: 10.48550/arXiv.2103.04970
- Shin, Y.-M., Tran, C., Shin, W.-Y., & Cao, X. (2021). Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes. http://arxiv.org/abs/2104.05225
- Son, H., Kim, C., & Turkan, Y. (2015, June 18). Scan-to-BIM - An Overview of the Current State of the Art and a Look Ahead. DOI: 10.22260/ISARC2015/0050
- Tang, P., Huber, D., Akinci, B., Lipman, R., & Lytle, A. (2010). Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques. In Automation in Construction (Vol. 19, Issue 7, pp. 829–843). Elsevier B.V. DOI: 10.1016/j.autcon.2010.06.007
- Veličković, P., Casanova, A., Liò, P., Cucurull, G., Romero, A., & Bengio, Y. (2017). Graph Attention Networks. 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings. DOI: 10.48550/arxiv.1710.10903
- Wang, Z., Sacks, R., & Yeung, T. (2022). Exploring graph neural networks for semantic enrichment: Room type classification. Automation in Construction, 134. DOI: 10.1016/j.autcon.2021.104039
- Xie, Y., Li, S., Liu, T., & Cai, Y. (2023). As-built BIM reconstruction of piping systems using PipeNet. Automation in Construction, 147. DOI: 10.1016/J.AUTCON.2022.104735
- Yin, C., Wang, B., Gan, V. J. L., Wang, M., & Cheng, J. C. P. (2021). Automated semantic segmentation of industrial point clouds using ResPointNet++. Automation in Construction, 130. DOI: 10.1016/j.autcon.2021.103874
Informazioni sul capitolo
Titolo del capitolo
Topological Relationship Modelling for Industrial Facility Digitisation Using Graph Neural Networks
Autori
Haritha Jayasinghe, Ioannis Brilakis
DOI
10.36253/979-12-215-0289-3.88
Opera sottoposta a peer review
Anno di pubblicazione
2023
Copyright
© 2023 Author(s)
Licenza d'uso
Licenza dei metadati
Informazioni bibliografiche
Titolo del libro
CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
Sottotitolo del libro
Managing the Digital Transformation of Construction Industry
Curatori
Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Opera sottoposta a peer review
Anno di pubblicazione
2023
Copyright
© 2023 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
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
Collana
Proceedings e report
ISSN della collana
2704-601X
e-ISSN della collana
2704-5846