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图学学报 ›› 2022, Vol. 43 ›› Issue (3): 522-529.DOI: 10.11996/JG.j.2095-302X.2022030522

• 建筑与城市信息模型 • 上一篇    下一篇

基于 BIM 和深度学习的建筑平面凹凸不规则识别

  

  1. 1. 上海交通大学船舶海洋与建筑工程学院,上海 200240;
    2. 上海市公共建筑和基础设施数字化运维重点实验室,上海 200240
  • 出版日期:2022-06-30 发布日期:2022-06-28
  • 基金资助:
    上海市住房和城乡建设管理委员会科技专项(2020-009-005);重庆市自然科学基金项目(CSTC2021JCYJ-MSXMX0986)

Identification of the plane irregularity of structures based on BIM and deep learning

  1. 1. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
    2. Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai 200240, China
  • Online:2022-06-30 Published:2022-06-28
  • Supported by:
    Shanghai Municipal Commission of Housing and Urban-rural Development Research Project (2020-009-005); Natural Science
    Foundation of Chongqing (CSTC2021JCYJ-MSXMX0986)

摘要:

建筑抗震超限审查是高层建筑、特别是超高层建筑审查的重要内容,建筑平面凹凸不规则是建筑抗震超限审查的项目之一。目前的建筑平面凹凸不规则识别主要由人工依据设计规范进行,然而日益复杂的建筑平面设计超出了规范的示例范围,也加重了人工审查的负担。建筑平面识别可以看成是图片分类问题,考虑到实际工程中规则样本和不规则样本之间的不均衡性,利用异常检测的思想,提出了一种基于建筑信息模型(BIM)和深度学习进行建筑平面凹凸不规则辅助识别的方法。首先,利用几何对象之间的布尔交运算得到 BIM模型的建筑平面;然后,通过图片预处理,生成建筑平面外轮廓图;最后,将建筑平面外轮廓图输入已训练好的异常检测深度学习模型,反馈识别结果。实验结果表明,相比于传统的图片分类模型,采用异常检测的思路对不规则建筑平面图的识别率提高了 15%,更符合实际工程的需要。

关键词: 建筑信息模型, 抗震审查, 深度学习, 异常检测, 建筑平面, 不规则识别

Abstract:

Compliance checking on earthquake-resistance is essential for architectures, especially for high-rise buildings. Current checking methods rely heavily on human efforts. In particular, as one of the critical checking contents, the identification of the plane irregularity of structures is time-consuming and error pone, because the building plane designs are becoming increasingly complex. The identification of the plane irregularity of structures could be regarded as a plane classification problem where the regular planes are identified as normal samples and the irregular ones as abnormal samples. Considering the unbalanced distribution of regular and irregular planes in construction projects and adopting the idea of an anomaly detection model, a methodology for the identification of the plane irregularity was proposed based on Building Information Modeling (BIM) and deep learning. Firstly, the building plane of BIM model was obtained by Boolean intersection operation between geometric objects. After image processing, the building plane could be converted into a building plane contour map. Finally, the trained anomaly detection model was executed on the contour map to yield the identification results. The experimental results show that in comparison with the traditional image classification models, the new one following the idea of an anomaly detection model can increase the identification rate of irregular building planes by 15%, more readily meeting the needs of practical applications.

Key words: building information modeling, anti-seismic checking, deep learning, anomaly detection, building plane;
irregularity identification

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