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Article

Investigation of Classification and Anomalies Based on Machine Learning Methods Applied to Large Scale Building Information Modeling

1
School of Mathematics and Statistics, Northwestern Polytechnical University, 127 Youyi West Road, Xi’an 710072, China
2
Kabandy, 4 Rue des Pères Blancs, 1040 Brussels, Belgium
*
Author to whom correspondence should be addressed.
Academic Editors: Jerry Chun-Wei Lin, Gautam Srivastava and Stefania Tomasiello
Appl. Sci. 2022, 12(13), 6382; https://doi.org/10.3390/app12136382
Received: 9 September 2021 / Revised: 6 December 2021 / Accepted: 15 June 2022 / Published: 23 June 2022
(This article belongs to the Special Issue Integrated Artificial Intelligence in Data Science)
Building Information Models (BIM) capable of collecting and synchronizing all the data related to a construction project into a unified numerical model consisting of a 3D representation and additional metadata (e.g., materials, physical properties, cost) have become commonplace in the building sector. Their extensive use today, alongside the increase in experience with BIM models, offers new perspectives and potentials for design and planning. However, large-scale complex data collection leads to two main challenges: the first is related to the automatic classification of BIM elements, namely windows, walls, beams, columns, etc., and the second to detecting abnormal elements without manual intervention, particularly in the case of misclassification. In this work, we propose machine learning for the automated classification of elements, and for the detection of anomalies based on geometric inputs and additional metadata properties that are extracted from the building model. More precisely, a Python program is used to decipher the BIM models (available as IFC files) for a series of complex buildings, and three types of machine learning methods are then tested to classify and detect objects from a large set of BIM data. The approach is tested on a variety of practical test cases. View Full-Text
Keywords: Building Information Modeling; data classification; data detection; machine learning Building Information Modeling; data classification; data detection; machine learning
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MDPI and ACS Style

Xiao, M.; Chao, Z.; Coelho, R.F.; Tian, S. Investigation of Classification and Anomalies Based on Machine Learning Methods Applied to Large Scale Building Information Modeling. Appl. Sci. 2022, 12, 6382. https://doi.org/10.3390/app12136382

AMA Style

Xiao M, Chao Z, Coelho RF, Tian S. Investigation of Classification and Anomalies Based on Machine Learning Methods Applied to Large Scale Building Information Modeling. Applied Sciences. 2022; 12(13):6382. https://doi.org/10.3390/app12136382

Chicago/Turabian Style

Xiao, Manyu, Zhiqin Chao, Rajan F. Coelho, and Shaobo Tian. 2022. "Investigation of Classification and Anomalies Based on Machine Learning Methods Applied to Large Scale Building Information Modeling" Applied Sciences 12, no. 13: 6382. https://doi.org/10.3390/app12136382

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