A high-performance BIM component element detection model

YiFei Wang; YiShi Wang; GaoDa Wei

doi:10.1117/12.2659345

22 November 2022 A high-performance BIM component element detection model

YiFei Wang, YiShi Wang, GaoDa Wei

Proceedings Volume 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022); 1247514 (2022) https://doi.org/10.1117/12.2659345
Event: Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 2022, Hulun Buir, China

Abstract

IFC (Industry Foundation Classes) is a standard format for information exchange developed by Building SMART, dedicated to the collaborative work of various software in architectural design, construction and operation and maintenance. With IFC standard for various BIM (Building Information Modeling), the software provides a unified data structure and file exchange format for data exchange. However, lacking formal rigidity, data exchange is often arbitrary and prone to errors, omissions, and misrepresentations. This study applies the machine learning technique LightGBM to examine BIM elements and IFC The accuracy of the mapping between classes is extracted through feature engineering, and the BIM model element detection model is constructed. By using the BIM model training set for training, the results show that our model is more than 97.8 % accurate. And compared to the popular machine learning models, our model has higher performance.

Citation Download Citation

YiFei Wang, YiShi Wang, and GaoDa Wei "A high-performance BIM component element detection model", Proc. SPIE 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 1247514 (22 November 2022); https://doi.org/10.1117/12.2659345

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