A BIM component element detection model based on machine learning

Hanbing Ma

doi:10.1117/12.3038113

7 August 2024 A BIM component element detection model based on machine learning

Hanbing Ma

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 1322920 (2024) https://doi.org/10.1117/12.3038113
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

The widespread adoption of Building Information Model (BIM) technology has profoundly impacted the architecture, engineering, and construction (AEC) industry, with the Industry Foundation Classes (IFC) serving as a pivotal open-standard file format facilitating BIM data exchange. Despite IFC's significant advantages in promoting interoperability and data sharing, challenges such as complexity, performance issues, and potential information loss persist. This study explores the utilization of the IFCNetCore dataset and training with Vision Transformer (VIT) models to process IFC images and achieve intelligent classification of BIM elements. Compared to traditional Convolutional Neural Networks (CNNs) like ResNet152, the proposed approach exhibits superior robustness and generalization, thereby enhancing efficiency in architectural design, construction, and management while fostering support for the development of intelligent buildings and virtual simulation domains.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hanbing Ma "A BIM component element detection model based on machine learning", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 1322920 (7 August 2024); https://doi.org/10.1117/12.3038113

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