Browse > Article
http://dx.doi.org/10.12652/Ksce.2021.41.3.0277

Modeling Element Relations as Structured Graphs Via Neural Structured Learning to Improve BIM Element Classification  

Yu, Youngsu (Seoul National University of Science and Technology)
Lee, Koeun (Seoul National University of Science and Technology)
Koo, Bonsang (Seoul National University of Science and Technology)
Lee, Kwanhoon (Korea University)
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.41, no.3, 2021 , pp. 277-288 More about this Journal
Abstract
Building information modeling (BIM) element to industry foundation classes (IFC) entity mappings need to be checked to ensure the semantic integrity of BIM models. Existing studies have demonstrated that machine learning algorithms trained on geometric features are able to classify BIM elements, thereby enabling the checking of these mappings. However, reliance on geometry is limited, especially for elements with similar geometric features. This study investigated the employment of relational data between elements, with the assumption that such additions provide higher classification performance. Neural structured learning, a novel approach for combining structured graph data as features to machine learning input, was used to realize the experiment. Results demonstrated that a significant improvement was attained when trained and tested on eight BIM element types with their relational semantics explicitly represented.
Keywords
BIM; IFC; Semantic integrity; ANN; NSL;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Jung, R. K., Koo, B. S. and Yu, Y. S. (2019). "Using deep learning for automated classification of wall subtypes for semantic integrity checking of building information models." KIBIM Magazine, Vol. 9, No. 4, pp. 31-40 (in Korean).
2 Venugopal, M., Eastman, C. M., Sacks, R. and Teizer, J. (2012). "Semantics of model views for information exchanges using the industry foundation class schema." Advanced Engineering Informatics, Vol. 26, No. 2, pp. 411-428.   DOI
3 Wu, J. and Zhang, J. (2019). "New automated BIM object classification method to support BIM interoperability." Journal of Computing in Civil Engineering, Vol. 33, No. 5, 04019033.   DOI
4 Shin, J. H., Kwon, S. W., Lee, K. H., Choi, S. D. and Kim, J. M. (2015). "A study of the establishment of framework for information exchange based on IFC model in domestic collaborative design environment." Korean Journal of Construction Engineering and Management, Vol. 16, No. 1, pp. 24-34. (in Korean).   DOI
5 Ma, L., Sacks, R. and Kattell, U. (2017). "Building model object classification for semantic enrichment using geometric features and pairwise spatial relations." 2017 Lean and Computing in Construction Congress (LC3), Heraklion, Crete, Greece, Vol. 1, pp. 373-380.
6 Koo, B. S. and Shin, B. J. (2018). "Applying novelty detection to identify model element to IFC class misclassifications on architectural and infrastructure building information models." Journal of Computational Design and Engineering, Vol. 5, No. 4, pp. 391-400.   DOI
7 Lomio, F., Farinha, R., Laasonen, M. and Huttunen, H. (2018). "Classification of building information model (BIM) structures with deep learning." 2018 7th European Workshop on Visual Information Processing (EUVIP), IEEE, Tampere, Finland, pp. 1-6.
8 Kipf, T. N. and Welling, M. (2016). "Semi-supervised classification with graph convolutional networks." Published as a Conference Paper at ICLR 2017, Toulon, France, arXiv:1609.02907.
9 Koo, B. S., La, S. M., Cho, N. W. and Yu, Y. S. (2019). "Using support vector machines to classify building elements for checking the semantic integrity of building information models." Automation in Construction, Vol. 98, No. 15, pp. 183-194.   DOI
10 Koo, B. S., Yu, Y. S. and Jung, R. K. (2018). "Machine learning based approach to building element classification for semantic integrity checking of building information models." Korean Journal of Computational Design and Engineering, Vol. 23, No. 4, pp. 373-383 (in Korean).   DOI
11 Pauwels, P. and Terkaj, W. (2016). "EXPRESS to OWL for construction industry: Towards a recommendable and usable ifcOWL ontology." Automation in Construction, Vol. 63, pp. 100-133.   DOI
12 Sacks, R., Ma, L., Yosef, R., Borrmann, A., Daum, S. and Kattel, U. (2017). "Semantic enrichment for building information modeling: Procedure for compiling inference rules and operators for complex geometry." Journal of Computing in Civil Engineering, Vol. 31, No. 6, 04017062.   DOI
13 Bloch, T. and Sacks, R. (2018). "Comparing machine learning and rule-based inferencing for semantic enrichment of BIM models." Automation in Construction, Vol. 91, No. 21, pp. 256-272.   DOI
14 Su, W., Yuan, Y. and Zhu, M. (2015, September). "A relationship between the average precision and the area under the roc curve." In Proceedings of the 2015 International Conference on The Theory of Information Retrieval, Northampton, Massachusetts, pp. 349-352.
15 TensorFlow (2020). The neural structured learning framework, Available at: https://www.tensorflow.org/neural_structured_learning/framework?hl=ko (Accessed: July 4, 2020).
16 Bassier, M., Vergauwen, M. and Van Genechten, B. (2017). "Automated classification of heritage buildings for as-built BIM using machine learning techniques." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 4-2, No. W2, pp. 25-30.
17 Bazjanac, V. and Kiviniemi, A. (2007). "Reduction, simplification, translation and interpretation in the exchange of model data." International Council for Research and Innovation in Building and Construction w78 Conference, Cape Town, South Africa, Vol. 78, pp. 163-168.
18 Belsky, M., Sacks, R. and Brilakis, I. (2016). "Semantic enrichment for building information modeling." Computer-Aided Civil and Infrastructure Engineering, Vol. 31, No. 4, pp. 261-274.   DOI
19 Brilakis, I., Belsky, M. and Sacks, R. (2014). "A semantic enrichment engine for building information modelling." Computer-Aided Civil and Infrastructure Engineering, Vol. 31, pp. 261-274.
20 Bui, T. D., Ravi, S. and Ramavajjala, V. (2018). "Neural graph learning: Training neural networks using graphs." Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, Marina del Rey, California, pp. 64-71.
21 Kim, J. S., Song, J. Y. and Lee, J. K. (2019). "Recognizing and classifying unknown object in BIM using 2D CNN." International Conference on Computer-Aided Architectural Design Futures, pp. 47-57 (in Korean).
22 Cursi, S., Simeone, D. and Coraglia, U. M. (2017). "An ontology-based platform for BIM semantic enrichment." Proceedings of the 35th eCAADe Conference, Vol. 2, Rome, Italy, pp. 649-656.
23 Eastman, C., Lee, J. M., Jeong, Y. S. and Lee, J. K. (2009). "Automatic rule-based checking of building designs." Automation in Construction, Vol. 18, No. 8, pp. 1011-1033.   DOI