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Building Adaption Model in Assessing Adaption Potential of Old Residential Quarters Read chapter Read first chapter

2021 | OriginalPaper | Chapter

A Machine Learning-Based Approach for BIM Object Localization

Authors : Jing Wang, Weisheng Lu, Fan Xue, Meng Ye

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

Publisher: Springer Singapore

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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.

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Metadata
Title
A Machine Learning-Based Approach for BIM Object Localization
Authors
Jing Wang
Weisheng Lu
Fan Xue
Meng Ye
Copyright Year
2021
Publisher
Springer Singapore
Book
Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate

Print ISBN: 978-981-15-8891-4

Electronic ISBN: 978-981-15-8892-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-8892-1_97