$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

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

Journal of KIBIM = 한국BIM학회논문집, v.12 no.2, 2022년, pp.12 - 25  

김시현 (서울과학기술대학교 건설시스템공학과) ,  이원복 (서울과학기술대학교 건설시스템공학과) ,  유영수 (서울과학기술대학교 건설시스템공학과) ,  구본상 (서울과학기술대학교 건설시스템공학과)

Abstract AI-Helper 아이콘AI-Helper

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 i...

주제어

표/그림 (25)

참고문헌 (30)

  1. Ajayakumar, K. (2021). Classification of the Level of Geometry of Building Elements using Deep-learning, https://www.cms.bgu.tum.de/en/theses/completedtheses (Dec. 15.2021) 

  2. Bienvenido-Huertas, D., Nieto-Julian, J.E., Moyano, J.J., Macias-Bernal, J.M., Castro, J. (2019). Implementing Artificial Intelligence in H-BIM Using the J48 Algorithm to Manage Historic Buildings. International Journal of Architectural Heritage, 14, pp. 1148-1160. 

  3. Bloch, T., Sacks, R. (2018). Comparing machine learning and rule-based inferencing for semantic enrichment of BIM models, Automation in Construction, 91, pp. 256-272. 

  4. Cursi, S., Simeone, D., Coraglia, U. M. (2017). An ontology-based platform for BIM semantic enrichment. Proceedings of the 35th eCAADe Conference, 2, pp. 649-656. 

  5. Dietterich, T. G. (2002). Ensemble learning. The handbook of brain theory and neural networks, 2(1), pp. 110-125. 

  6. Eastman, C., Lee, J. M., Jeong, Y. S., Lee, J. K. (2009). Automatic rule-based checking of building designs. Automation in Construction, 18(8), 1011-1033. 

  7. Eastman, C. M., Jeong, Y. S., Sacks, R., Kaner, I. (2010). Exchange model and exchange object concepts for implementation of national BIM standards, Journal of computing in civil engineering, 24(1), pp. 25-34. 

  8. Eom, H. N., Kim, J. S., Choi, S. O. (2020). Machine learning-based corporate default risk prediction model verification and policy recommendation: Focusing on improvement through stacking ensemble model, Journal of Intelligence and Information Systems, 26(2), pp. 105-129. 

  9. Hwang, J. R., Kang, T. W., Hong, C. H. (2012). A Study on The Correlation Analysis Between IFC and CityGML for Efficient Utilization of Construction Data and GIS Data, Journal of Korea Spatial Information Society, 20(5), pp. 49-56. 

  10. Jung, R. K., Koo, B. S., Yu, Y. S. (2019). Using Deep Learning for Automated Classification of Wall Subtypes for Semantic Integrity Checking of Building Information Models, Journal of KBIM, 9(4), pp. 31-40. 

  11. Khemlani, L. (2004). The IFC Building Model: A Look Under the Hood, AECbytes, https://www.aecbytes.com/feature/2004/IFC.html (Nov, 16, 2021) 

  12. Kim, I. H., Yoo, H. J., Choi, J. S. (2012). A Study on the Interoperability Improvement of IFC Property Information for Energy Performance Assessment in the Early Design Phase, Transactions of the Society of CAD/CAM Engineers, 27(6), pp. 456-465. 

  13. Koo, B. S., Fischer, M. (2000). Feasibility study of 4D CAD in commercial construction, Journal of construction engineering and management, 126(4), pp. 251-260. 

  14. Koo, B. S., Yu, Y. S., Jung, R. K. (2018). Machine Learning Based Approach to Building Element Classification for Semantic Integrity Checking of Building Information Models, Korean Journal of Computational Design and Engineering, 23(4), pp.373-383. 

  15. Koo, B., Jung, R., Yu, Y. (2021). Automatic classification of wall and door BIM element subtypes using 3D geometric deep neural networks. Advanced Engineering Informatics, 47, 101200. 

  16. Krijnen, T. (2015). IfcOpenShell, https://ifcopenshell.org (Oct. 15. 2021) 

  17. Lee, J. Y., Seo, M. R., Son, B. S. (2009). A Study on the Exchange Method of Building Information Model between BIM Solutions using IFC File Format, Journal of the Architectural Institute of Korea Planning and Design, 25(3), pp. 29-38. 

  18. Lee, J., Park, J., Yoon, H. (2020). Automatic Classification of Bridge Component based on Deep Learning. Journal of the Korean Society of Civil Engineers, 40(2), pp. 239-245. 

  19. Ma, L., Sacks, R., Kattell, U. (2017). Building model object classification for semantic enrichment using geometric features and pairwise spatial relations, Proceedings of the Joint Conference on Computing in Construction, 1, pp. 373-380. 

  20. Maturana, D., Scherer, S. (2015). Voxnet: A 3d convolutional neural network for real-time object recognition. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 922-928). IEEE. 

  21. Park, J. D., Jeong, Y. W. (2010). A Study on the Ontology-Based Representation Model for Interoperability of BIM(Building Information Model), Journal of the Architectural Institute of Korea Planning and Design, 26(8), pp. 21-28. 

  22. Polikar, R. (2006). Ensemble based systems in decision making, IEEE Circuits and systems magazine, 6(3), pp. 21-45. 

  23. Qi, C. R., Su, H., Mo, K., Guibas, L. J. (2017). Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652-660. 

  24. Ramos, J. (2003). Using tf-idf to determine word relevance in document queries, In Proceedings of the first instructional conference on machine learning, 242(1), pp. 29-48. 

  25. Rokach, L. (2010). Ensemble-based classifiers. Artificial intelligence review, 33(1), pp. 1-39. 

  26. Shen, J. (2020). A Simulated Point Cloud Implementation of a Machine Learning Segmentation and Classification Algorithm (Doctoral dissertation, Purdue University Graduate School). 

  27. Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E. (2015). Multi-view convolutional neural networks for 3d shaperecognition, Proceedings of the IEEE internationalconference on computer vision, pp. 945-953. 

  28. Wang, C., Cho, Y. K., Kim, C. (2015). Automatic BIM component extraction from point clouds of existing buildings for sustainability applications. Automation in Construction, 56, 1-13. 

  29. Xu, N., Luo, J., Wu, T., Dong, W., Liu, W., Zhou, N. (2021). Identification and portrait of urban functional zones based on multisource heterogeneous data and ensemble learning, Remote Sensing, 13(3), 373. 

  30. Yu, Y. S., Lee, K. E., Koo, B. S., Lee, K. H. (2021). Modeling Element Relations as Structured Graphs Via Neural Structured Learning to Improve BIM Element Classification, Journal of Civil and Environmental Engineering Research, 41(3), pp. 227-288. 

저자의 다른 논문 :

관련 콘텐츠

오픈액세스(OA) 유형

BRONZE

출판사/학술단체 등이 한시적으로 특별한 프로모션 또는 일정기간 경과 후 접근을 허용하여, 출판사/학술단체 등의 사이트에서 이용 가능한 논문

이 논문과 함께 이용한 콘텐츠

저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로