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A BIM-BASED IDENTIFICATION AND CLASSIFICATION METHOD OF ENVIRONMENTAL RISKS IN THE DESIGN OF BEIJING SUBWAY.
- Source :
- Journal of Civil Engineering & Management; 2021, Vol. 27 Issue 7, p500-514, 15p
- Publication Year :
- 2021
-
Abstract
- The subway project safety risk management in China covers design and construction stages. The traditional method for identifying construction safety risks at the design stage requires that engineers work backward, and it relies on engineers having an accurate understanding of complex engineering information, spatial relationships, and rich experience. This paper proposes a Building Information Modeling (BIM) based automatic identification and classification framework for environmental risks at the subway design stage. First, a database of discriminant rules was established in order to achieve the digital expression of the discriminant standards for environmental risks. Second, environmental models and discriminant models were created in order to analyze spatial collisions. Then, the risk discrimination algorithm was embedded in the BIM platform. The program automatically analyzed the collision result-based discrimination rule database and output a list of environmental risks associated with the model. Finally, a shield tunnel was used for practice. As a result, the BIM-based method for automatically identifying environmental risks could improve the efficiency of the special design of safety risks and promote the digital transmission of risk design information throughout the construction process. The method described in this paper provides a reference for the safety risk management technology system in China's subway projects. This method can also be applied to projects such as underground pipe gallery, power tunnel, and foundation pit, after optimizing the classification rules. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13923730
- Volume :
- 27
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- Journal of Civil Engineering & Management
- Publication Type :
- Academic Journal
- Accession number :
- 153060048
- Full Text :
- https://doi.org/10.3846/jcem.2021.15602