Knowledge Extraction and Discovery Based on BIM: A Critical Review and Future Directions

Archives of Computational Methods in Engineering, 2021

Recommended citation: Hu, Z.Z.*, Leng, S., Lin, J.R., Li, S.W., Xiao, Y.Q. (2021). Knowledge Extraction and Discovery Based on BIM: A Critical Review and Future Directions. Archives of Computational Methods in Engineering, 29(1), 335-356. doi: 10.1007/s11831-021-09576-9 cited by count


In the past, knowledge in the fields of Architecture, Engineering and Construction (AEC) industries mainly come from experiences and are documented in hard copies or specific electronic databases. In order to make use of this knowledge, a lot of studies have focused on retrieving and storing this knowledge in a systematic and accessible way. The Building Information Modeling (BIM) technology proves to be a valuable media in extracting data because it provides physical and functional digital models for all the facilities within the life-cycle of the project. Therefore, the combination of the knowledge science with BIM shows great potential in constructing the knowledge map in the field of the AEC industry. Based on literature reviews, this article summarizes the latest achievements in the fields of knowledge science and BIM, in the aspects of (1) knowledge description, (2) knowledge discovery, (3) knowledge storage and management, (4) knowledge inference and (5) knowledge application, to show the state-of-arts and suggests the future directions in the application of knowledge science and BIM technology in the fields of AEC industries. The review indicates that BIM is capable of providing information for knowledge extraction and discovery, by adopting semantic network, knowledge graph and some other related methods. It also illustrates that the knowledge is helpful in the design, construction, operation and maintenance periods of the AEC industry, but now it is only at the beginning stage.

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This study was funded by the National Natural Science Foundation of China (No. 51778336) and the Tsinghua University-Glodon Joint Research Centre for Building Information Model (RCBIM).

Accession Number: WOS:000639061200001

ISSN: 1134-3060

eISSN: 1886-1784

IDS Number: RL6DL

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