Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects
Source abstract
Abstract
Safety risk identification throughout deep excavation construction is an information-intensive task, involving construction information scattered in project planning documentation and dynamic information obtained from different field sensors. However, inefficient information integration and exchange have been an important obstacle to the development of automatic safety risk identification in actual applications. This research aims to achieve the requirements for information integration and exchange by developing a semantic industry foundation classes (IFC) data model based on a central database of Building Information Modeling (BIM) in dynamic deep excavation process. Construction information required for risk identification in dynamic deep excavation is analyzed. The relationships among construction information are identified based on the semantic IFC data model, involved relationships (i.e., logical relationships and constraints among risk events, risk factors, construction parameters, and construction phases), and BIM elements. Furthermore, an automatic safety risk identification approach is presented based on the semantic data model, and it is tested through a construction risk identification prototype established under the BIM environment. Results illustrate the effectiveness of the BIM-based central database in accelerating automatic safety risk identification by linking BIM elements and required construction information corresponding to the dynamic construction process.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: BIM and Construction Integration · Infrastructure Maintenance and Monitoring · Occupational Health and Safety Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Management Engineering, Huaiyin Institute of Technology, Huaian 223003, China
Department of Construction Management, Huazhong University of Science and Technology, Wuhan 430074, China
Department of Civil Engineering, Aston University, Birmingham B4 7ET, UK
Funding
National Natural Science Foundation of China · Jiangsu Department of Housing and Construction project · Jiangsu Academy of Productivity Science project · Jiangsu Smart Factory Engineering Research Center project
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