Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects

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.

Publication DOI: https://doi.org/10.3390/app11219958
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Engineering Systems and Supply Chain Management
Additional Information: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/) Funding: This research was funded by the National Natural Science Foundation of China, grant number 71901104; Jiangsu Department of Housing and Construction project, grant number 2019ZD001085; Jiangsu Academy of Productivity Science project, grant number JSSCL2020A010; and Jiangsu Smart Factory Engineering Research Center project, grant number JSSFER2019A3.
Uncontrolled Keywords: BIM,Deep excavation,IFC schema,Risk,Safety,Materials Science(all),Instrumentation,Engineering(all),Process Chemistry and Technology,Computer Science Applications,Fluid Flow and Transfer Processes
Publication ISSN: 2076-3417
Last Modified: 05 Jun 2024 07:19
Date Deposited: 27 Oct 2021 10:33
Full Text Link:
Related URLs: https://www.mdp ... 3417/11/21/9958 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-10-25
Accepted Date: 2021-10-22
Authors: Zhang, Yongcheng
Xing, Xuejiao
Antwi-Afari, Maxwell Fordjour (ORCID Profile 0000-0002-6812-7839)

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