Intelligent BIM Searching via Deep Embedding of Geometric, Semantic, and Topological Features
As a digital representation of buildings, building information models (BIMs) encapsulate geometric, semantic, and topological features (GSTFs), to express the visual and functional characteristics of building components and their connections to create building systems. However, searching for BIMs pa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/6/951 |
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| author | Pin-Hao Huang Sheng-Yu Song Zhen Xu Zhen-Zhong Hu Jia-Rui Lin |
| author_facet | Pin-Hao Huang Sheng-Yu Song Zhen Xu Zhen-Zhong Hu Jia-Rui Lin |
| author_sort | Pin-Hao Huang |
| collection | DOAJ |
| description | As a digital representation of buildings, building information models (BIMs) encapsulate geometric, semantic, and topological features (GSTFs), to express the visual and functional characteristics of building components and their connections to create building systems. However, searching for BIMs pays much attention to semantic features, while overlooking geometric and topological features, making it difficult to find and reuse rich knowledge in BIMs. Thus, this study proposes a novel approach to intelligent BIM searching by embedding GSTFs via deep learning (DL). First, algorithms for extracting GSTFs from BIMs and identifying required GSTFs from search queries are developed. Then, different GSTFs are embedded via DL models, creating vector-based representations of BIMs or search queries. Finally, similarity-based ranking is adopted to find BIMs highly related to the queries. Experiments show that the proposed approach demonstrates an efficiency of 780 times greater than manual retrieval methods and 4–6% more efficient than traditional methods. This study advances the field of BIM searching by providing a more comprehensive, accurate, and efficient method for finding and reusing rich knowledge in BIMs, ultimately contributing to better building design and knowledge management. |
| format | Article |
| id | doaj-art-ca4d9557347d46ebbd028f705a98fbf8 |
| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-ca4d9557347d46ebbd028f705a98fbf82025-08-20T02:11:12ZengMDPI AGBuildings2075-53092025-03-0115695110.3390/buildings15060951Intelligent BIM Searching via Deep Embedding of Geometric, Semantic, and Topological FeaturesPin-Hao Huang0Sheng-Yu Song1Zhen Xu2Zhen-Zhong Hu3Jia-Rui Lin4Department of Civil Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Civil Engineering, Tsinghua University, Beijing 100084, ChinaSchool of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaShenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaDepartment of Civil Engineering, Tsinghua University, Beijing 100084, ChinaAs a digital representation of buildings, building information models (BIMs) encapsulate geometric, semantic, and topological features (GSTFs), to express the visual and functional characteristics of building components and their connections to create building systems. However, searching for BIMs pays much attention to semantic features, while overlooking geometric and topological features, making it difficult to find and reuse rich knowledge in BIMs. Thus, this study proposes a novel approach to intelligent BIM searching by embedding GSTFs via deep learning (DL). First, algorithms for extracting GSTFs from BIMs and identifying required GSTFs from search queries are developed. Then, different GSTFs are embedded via DL models, creating vector-based representations of BIMs or search queries. Finally, similarity-based ranking is adopted to find BIMs highly related to the queries. Experiments show that the proposed approach demonstrates an efficiency of 780 times greater than manual retrieval methods and 4–6% more efficient than traditional methods. This study advances the field of BIM searching by providing a more comprehensive, accurate, and efficient method for finding and reusing rich knowledge in BIMs, ultimately contributing to better building design and knowledge management.https://www.mdpi.com/2075-5309/15/6/951building information model (BIM)model searchfeature extractionsimilarity calculationdeep learningfeature embedding |
| spellingShingle | Pin-Hao Huang Sheng-Yu Song Zhen Xu Zhen-Zhong Hu Jia-Rui Lin Intelligent BIM Searching via Deep Embedding of Geometric, Semantic, and Topological Features Buildings building information model (BIM) model search feature extraction similarity calculation deep learning feature embedding |
| title | Intelligent BIM Searching via Deep Embedding of Geometric, Semantic, and Topological Features |
| title_full | Intelligent BIM Searching via Deep Embedding of Geometric, Semantic, and Topological Features |
| title_fullStr | Intelligent BIM Searching via Deep Embedding of Geometric, Semantic, and Topological Features |
| title_full_unstemmed | Intelligent BIM Searching via Deep Embedding of Geometric, Semantic, and Topological Features |
| title_short | Intelligent BIM Searching via Deep Embedding of Geometric, Semantic, and Topological Features |
| title_sort | intelligent bim searching via deep embedding of geometric semantic and topological features |
| topic | building information model (BIM) model search feature extraction similarity calculation deep learning feature embedding |
| url | https://www.mdpi.com/2075-5309/15/6/951 |
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