3D object recognition using deep learning for automatically generating semantic BIM data

Kay Rogage*, Omar Doukari*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The successful reuse of Building Information Model (BIM) data is reliant on the use of clearly defined objects. File formats such as the Industry Foundation Classes (IFC) along with classification systems offer approaches to standardising semantic BIM data. Inconsistent application of these standards during BIM authoring results in objects that are unable to be reused across systems. This research proposes a Machine Learning (ML) based alternative for detecting objects and enriching BIM data based on geometrical representations of objects to support interoperability across systems throughout the supply chain. First, an existing dataset of training and test data is modified to correspond with predefined data types from the IFC schema. Second, a convolutional deep belief network is implemented and applied on new testing data from the National BIM Library to identify 3D objects and enabling semantic enrichment of BIM data. Finally, the testing is extended to four real-world industry BIM models. The findings show that the geometric data from emerging BIM object libraries can be used to search and retrieve objects within industry BIM models based on geometric rather than semantic data.
Original languageEnglish
Article number105366
Number of pages13
JournalAutomation in Construction
Volume162
Early online date13 Mar 2024
DOIs
Publication statusE-pub ahead of print - 13 Mar 2024

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