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Automatic layer classification method-based elevation recognition in architectural drawings for reconstruction of 3D BIM models

机译:基于自动图层分类方法的建筑图纸中高程识别,用于3D BIM模型的重建

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摘要

Automatic interpretation of computer aided design drawings into a Building Information Model (BIM) would reduce the time and labor costs involved in the modeling process for construction parties. However, the offset and height of building objects cannot be automatically extracted by current algorithms and products since they mostly focus on floor plan detection whilst such information conventionally appears on elevation drawings. Manual inspection and input are needed, which is rigid, error prone, and costly. The challenge of elevation recognition is attributed to the irregular and intricate shapes of the objects portrayed in elevation views, which make it difficult to fully cluster the primitives composing a building object. Additionally, none of the existing methods includes floor plan detection and elevation detection to enable a comprehensive reconstruction of a 3D BIM model. In this paper, these issues are tackled by resorting to an automatically layer classification method (ALCM) that identifies the content of hidden layers. An ALCM-based elevation recognition method is developed. It recognizes the orientation of elevation views and levels of each floor. Furthermore, it segments openings (windows and doors) in elevation views and outputs their offset and height dimensions. A facade BIM model is generated with all openings placed at the correct offsets. The experiments take 94 different sample drawings to validate the model's performance. The test results demonstrate that nearly all floor levels are detected. And that 88% of the members that are visible in elevation drawings are measured perfectly. A real-world campus building is automatically modelled as a case study. The results imply that ALCM-EDM (Elevation Detection Method) contributes to the automatic conversion process since manual input of elevation data is avoided. Future directions could address on incorporating section views and detailed drawings into the reconstruction.
机译:将计算机辅助设计图自动解释为建筑信息模型(BIM)将减少施工方建模过程中涉及的时间和人工成本。但是,当前对象和算法通常无法自动提取建筑对象的偏移量和高度,因为它们主要关注平面图检测,而此类信息通常会显示在立面图上。需要手动检查和输入,这是严格的,容易出错的并且成本很高。高程识别的挑战归因于在高程视图中描绘的对象的不规则且复杂的形状,这使得很难将构成建筑对象的图元完全聚类。此外,现有方法均未包含平面图检测和高程检测以实现3D BIM模型的全面重建。在本文中,这些问题是通过使用自动层分类方法(ALCM)来识别隐藏层的内容来解决的。开发了一种基于ALCM的海拔识别方法。它可以识别每个楼层的立面视图和水平方向。此外,它在立面视图中分割开口(门窗)并输出其偏移量和高度尺寸。生成的外观BIM模型的所有开口均以正确的偏移放置。实验采用94个不同的样本图来验证模型的性能。测试结果表明几乎可以检测到所有楼层。并且在立面图中可见的88%的成员都经过了完美测量。现实世界中的校园建筑将自动建模为案例研究。结果表明,由于避免了手动输入高程数据,ALCM-EDM(高程检测方法)有助于自动转换过程。未来的方向可能涉及将剖面图和详细图纸纳入重建。

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  • 来源
    《Automation in construction》 |2020年第5期|103082.1-103082.19|共19页
  • 作者

  • 作者单位

    Univ Hong Kong Fac Architecture Dept Real Estate & Construct Hong Kong Peoples R China;

    Univ Nottingham Ningbo China Dept Architecture & Built Environm Ningbo Peoples R China;

    Natl Univ Singapore Dept Bldg Performance & Sustainabil Singapore Singapore;

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  • 正文语种 eng
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