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Clustering of BIM components based on similarity measurement of attributes

  

  1. (1. Institute of Computing Technology, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China; 
    2. School of Software, BNRist, Tsinghua University, Beijing 100084, China)
  • Online:2020-04-30 Published:2020-05-15

Abstract: In recent years, resources in the Building Information Modeling (BIM) components library are expanding rapidly on the Internet. There is an increasing demand for ways to cluster and retrieve appropriate BIM components among countless resources. However, the way to extract domain information of BIM components still can not be found in existing methods. This paper studies the clustering of BIM components based on the domain information of BIM components: ①For BIM components, tan algorithm measuring similarity is proposed based on the attribute information. Compared with the traditional Tversky similarity measure algorithm and geometry similarity matching algorithm, the newly proposed one the present study has produced a better result. ②A clustering method of BIM component library is proposed based on the similarity measure algorithm of BIM components. Users are provided with diverse ways to retrieve and check information thanks to the search engine of BIMSeek integrated with functions of keyword-based retrieval and classifier view. Compared with the K-medoids algorithm and AP algorithm, the results of ours are more desirable.

Key words: building information modeling, industry foundation class, information retrieval, similarity measure, clustering