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A Machine Learning-Based Approach for BIM Object Localization

In: Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate

Author

Listed:
  • Jing Wang

    (The University of Hong Kong)

  • Weisheng Lu

    (The University of Hong Kong)

  • Fan Xue

    (The University of Hong Kong)

  • Meng Ye

    (The University of Hong Kong)

Abstract

This research is positioned in the growing need for Building Information Modelling (BIM) localization to effectively use global BIM resources in a locality. It focuses on BIM objects, which are not only the primary ‘building blocks’ of modelling but also the fundamental elements conveying the BIM information. The problem here is that BIM objects from global libraries may contain general, ambiguous, inconsistent, and missing information, thus incurring considerable manual adjustment efforts to use BIM objects of this kind in local projects. This paper aims to propose a machine learning (ML)-based approach to automatically localize (i.e., enrich and modify) BIM objects and their associated information to suit the local needs. The approach comprises of three steps: (1) characterizing a BIM object; (2) developing a local object configurator (LOC) based on ML; and (3) training, calibrating, and applying the LOC for bulk BIM objects localization. This study contributes a methodological framework to develop the ML approach for BIM object localization. The outcomes of the study can also boost the development of local BIM object libraries at both industry and company level.

Suggested Citation

  • Jing Wang & Weisheng Lu & Fan Xue & Meng Ye, 2021. "A Machine Learning-Based Approach for BIM Object Localization," Springer Books, in: Gui Ye & Hongping Yuan & Jian Zuo (ed.), Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate, pages 1391-1399, Springer.
    • Handle: RePEc:spr:sprchp:978-981-15-8892-1_97
    DOI: 10.1007/978-981-15-8892-1_97
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    Cited by:

    1. Claudia ANTAL-VAIDA, 2021. "Basic Hyperparameters Tuning Methods for Classification Algorithms," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 25(2), pages 64-74.

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