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        Chapter Improving BIM Authoring Process Reproducibility with Enhanced BIM Logging

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        Author(s)
        Jang, Suhyung
        Lee, Ghang
        Language
        English
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        Abstract
        This paper presents an enhanced BIM logger designed to capture both geometry and attribute changes of building element geometries, thereby offering a transparent source of representation of the BIM authoring process. The authors developed the logger and reproduction algorithm using the Revit C# API based on the analysis of information required to define building elements and associated attributes. The enhanced BIM log was evaluated through a case study of Villa Savoye designed by Le Corbusier. Despite negligible discrepancies, the results show that the enhanced BIM log can accurately represent the BIM authoring process capturing and reproducing 92.45% of the building elements from the original BIM model. Future research can focus on expanding the scope of logging and probing the potential of automating the BIM authoring process using these enhanced BIM logs
        URI
        https://library.oapen.org/handle/20.500.12657/89083
        Keywords
        Building information modeling (BIM); BIM log mining; BIM authoring software; Custom BIM log; Authoring process reproducibility
        DOI
        10.36253/979-12-215-0289-3.49
        ISBN
        9791221502893, 9791221502893
        Publisher
        Firenze University Press
        Publisher website
        https://www.fupress.com/
        Publication date and place
        Florence, 2023
        Series
        Proceedings e report, 137
        Classification
        Virtualization
        Pages
        7
        Rights
        https://creativecommons.org/licenses/by-nc/4.0/legalcode
        • Imported or submitted locally

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        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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