Advanced Approach for Performance Improvement of Deep Learningbased BIM Elements Classification Model Using Ensemble Model

Si-Hyun Kim; Won-Bok Lee; Young-Su Yu; Bon-Sang Koo

doi:10.13161/kibim.2022.12.2.012

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2022 Vol.12, Issue 2 Preview Page Next Page

Advanced Approach for Performance Improvement of Deep Learning-based BIM Elements Classification Model Using Ensemble Model 딥러닝 기반 BIM 부재 자동분류 학습모델의 성능 향상을 위한 Ensemble 모델 구축에 관한 연구

30 June 2022. pp. 12-25

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Abstract

To increase the usability of Building Information Modeling (BIM) in construction projects, it is critical to ensure the interoperability of data between heterogeneous BIM software. The Industry Foundation Classes (IFC), an international ISO format, has been established for this purpose, but due to its structural complexity, geometric information and properties are not always transmitted correctly. Recently, deep learning approaches have been used to learn the shapes of the BIM elements and thereby verify the mapping between BIM elements and IFC entities. These models performed well for elements with distinct shapes but were limited when their shapes were highly similar. This study proposed a method to improve the performance of the element type classification by using an Ensemble model that leverages not only shapes characteristics but also the relational information between individual BIM elements. The accuracy of the Ensemble model, which merges MVCNN and MLP, was improved 0.03 compared to the existing deep learning model that only learned shape information.

Keywords

BIM

IFC

Semantic Enrichment

Ensemble

Relational Information

키워드

빌딩정보모델

IFC

시멘틱 강화

앙상블

관계정보

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Information

Publisher :Journal of KIBIM
Publisher(Ko) :한국BIM학회
Journal Title :Journal of KIBIM
Journal Title(Ko) :한국BIM학회논문집
Volume : 12
No :2
Pages :12-25
Received Date : 2022-02-04
Revised Date : 2022-05-04
Accepted Date : 2022-05-10
DOI :https://doi.org/10.13161/kibim.2022.12.2.012

Journal of KIBIM ISSN:2288-1697(Print) 2288-1697(Online) 한국BIM학회논문집

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