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    Semiautomatic Structural BIM-Model Generation Methodology Using CAD Construction Drawings

    Source: Journal of Computing in Civil Engineering:;2020:;Volume ( 034 ):;issue: 003
    Author:
    Bin Yang
    ,
    Boda Liu
    ,
    Dayu Zhu
    ,
    Binghan Zhang
    ,
    Zhichen Wang
    ,
    Ke Lei
    DOI: 10.1061/(ASCE)CP.1943-5487.0000885
    Publisher: ASCE
    Abstract: Limited to the current building information model (BIM) development stage, many engineers are still using CAD 2D drawings for top-down design, and then using drawings to generate BIM models for BIM-related applications. As the manual method of creating a semantic-rich BIM model is time consuming and could cause model defects, engineers are reluctant to apply the semantic-rich BIM model. To solve this problem, a semiautomatic methodology is proposed to generate a semantic-rich structural BIM model from two-dimensional (2D) computer-aided design (CAD) drawings. First, it uses the information classified by CAD layers to generate geometric models of beams, slabs, and columns. Then, the semantic information is appropriately structured and stored in the corresponding element models. Finally, the feasibility of this methodology is verified with two case studies of frame structures. The case studies show that this method can quickly and accurately create a structural BIM model with many elements that reflect the structural details described in the construction drawings. Nearly all text information in the construction drawings is accurately imported into the structural BIM model, and additional parameter spaces are reserved to facilitate the expansion of building information. The engineers in charge of creating a BIM model could benefit from this proposed methodology.
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      Semiautomatic Structural BIM-Model Generation Methodology Using CAD Construction Drawings

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4265253
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    contributor authorBin Yang
    contributor authorBoda Liu
    contributor authorDayu Zhu
    contributor authorBinghan Zhang
    contributor authorZhichen Wang
    contributor authorKe Lei
    date accessioned2022-01-30T19:24:48Z
    date available2022-01-30T19:24:48Z
    date issued2020
    identifier other%28ASCE%29CP.1943-5487.0000885.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4265253
    description abstractLimited to the current building information model (BIM) development stage, many engineers are still using CAD 2D drawings for top-down design, and then using drawings to generate BIM models for BIM-related applications. As the manual method of creating a semantic-rich BIM model is time consuming and could cause model defects, engineers are reluctant to apply the semantic-rich BIM model. To solve this problem, a semiautomatic methodology is proposed to generate a semantic-rich structural BIM model from two-dimensional (2D) computer-aided design (CAD) drawings. First, it uses the information classified by CAD layers to generate geometric models of beams, slabs, and columns. Then, the semantic information is appropriately structured and stored in the corresponding element models. Finally, the feasibility of this methodology is verified with two case studies of frame structures. The case studies show that this method can quickly and accurately create a structural BIM model with many elements that reflect the structural details described in the construction drawings. Nearly all text information in the construction drawings is accurately imported into the structural BIM model, and additional parameter spaces are reserved to facilitate the expansion of building information. The engineers in charge of creating a BIM model could benefit from this proposed methodology.
    publisherASCE
    titleSemiautomatic Structural BIM-Model Generation Methodology Using CAD Construction Drawings
    typeJournal Paper
    journal volume34
    journal issue3
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/(ASCE)CP.1943-5487.0000885
    page04020006
    treeJournal of Computing in Civil Engineering:;2020:;Volume ( 034 ):;issue: 003
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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