• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
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  • Chinese Science Citation Database
Volume 56 Issue 1
Jan.  2021
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Journal of Southwest Jiaotong University  > 2021  >  56(1): 176-181.
ZENG Jiadong, LI Mingshui, ZHANG Zhitian, CAO Baozhu. Separation-Reattachment Flows Characteristics of Rectangular High-Rise Buildings with Different Side Ratios[J]. Journal of Southwest Jiaotong University, 2020, 55(4): 780-788. doi: 10.3969/j.issn.0258-2724.20190259
Citation: SHI Haiou, YUAN Quan, ZHANG Yunlin, ZENG Wenqu, ZHENG Qing, DING Guofu. Multi-Discipline Forward Collaborative Design Technology Based on BIM Interaction and Data-Driven[J]. Journal of Southwest Jiaotong University, 2021, 56(1): 176-181. doi: 10.3969/j.issn.0258-2724.20200035
shu

Multi-Discipline Forward Collaborative Design Technology Based on BIM Interaction and Data-Driven

doi: 10.3969/j.issn.0258-2724.20200035
  • Received Date: 02 Feb 2020
  • Rev Recd Date: 03 Apr 2020
    • Available Online: 21 Oct 2020
  • Publish Date: 01 Feb 2021
    Abstract
  • In order to realize data-driven collaborative design among multiple disciplines and improve design efficiency, with the analysis of the forward collaborative design model of urban rail transits, the data-driven forward collaborative design technology is proposed for multiple disciplines. Firstly, the seven-dimensional building information model (BIM) that conforms to the characteristics of the urban rail transit is adopted. The three-dimensional design model is expressed by industry foundation class (IFC) and parsed to form the model structure tree. Then, the interactive component parameter design is completed through model interaction and data extraction. Finally, based on data-driven and collaborative information, the design parameters of collaborative components are adjusted, and the collaborative design process is completed. The case analysis shows that the data-driven forward collaborative design technology proposed in this work could effectively promote data interaction and coordination among the various disciplines of urban rail transit. It realizes the forward collaborative design among multiple disciplines, and contribute to forming an optimized design scheme and improving collaboration efficiency.

     

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