4.6 Article

IFC-Based Development of As-Built and As-Is BIMs Using Construction and Facility Inspection Data: Site-to-BIM Data Transfer Automation

期刊

出版社

ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)CP.1943-5487.0000727

关键词

-

资金

  1. Natural Science and Engineering Research Council [203368-2015, RGPIN-2017-06792]
  2. Center for Integrated Facility Engineering (CIFE) at Stanford University [2017-06]
  3. Stanford University

向作者/读者索取更多资源

Accurate and proactive inspection of constructed objects and identification of their defects and design conformity is vital to both facility management and construction projects. A project's building information model (BIM) needs to be regularly updated to more accurately reflect the actual site conditions. However, conventional practices lack site-data integration with models and rely on manual, error-prone, and costly updates. This paper proposes and demonstrates a robust technique that uses the industry foundation classes (IFC) schema to automatically update an as-designed BIM based on site observations for inspected building elements. It receives as input an inspected object's actual type and inspection details including the detected defects/changes, responsible actors, as-built/as-is type, captured images, and time and the date of the inspection. The algorithm automatically analyzes the IFC data model to retrieve the element's semantics and identifies discrepancies between the as-built/as-is and as-designed object conditions. Based on the results of the discrepancy analysis, the IFC data model is populated with new semantics, resulting in the update of object types, properties, and three-dimensional (3D) shape representations. Inspection details and user entries are automatically documented in the BIM and assigned to objects to enable potential diagnostics and tractability. This automates site-to-BIM data transfer and supports reality-capture techniques. (c) 2017 American Society of Civil Engineers.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据