Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/21/9958 |
| _version_ | 1797512884160299008 |
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| author | Yongcheng Zhang Xuejiao Xing Maxwell Fordjour Antwi-Afari |
| author_facet | Yongcheng Zhang Xuejiao Xing Maxwell Fordjour Antwi-Afari |
| author_sort | Yongcheng Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:07:29Z |
| format | Article |
| id | doaj.art-d91934ba90d54d67b281b1113a1f28ac |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:07:29Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d91934ba90d54d67b281b1113a1f28ac2023-11-22T20:25:38ZengMDPI AGApplied Sciences2076-34172021-10-011121995810.3390/app11219958Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation ProjectsYongcheng Zhang0Xuejiao Xing1Maxwell Fordjour Antwi-Afari2School of Management Engineering, Huaiyin Institute of Technology, Huaian 223003, ChinaDepartment of Construction Management, Huazhong University of Science and Technology, Wuhan 430074, ChinaDepartment of Civil Engineering, Aston University, Birmingham B4 7ET, UKSafety 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.https://www.mdpi.com/2076-3417/11/21/9958safetyriskBIMIFC schemadeep excavation |
| spellingShingle | Yongcheng Zhang Xuejiao Xing Maxwell Fordjour Antwi-Afari Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects Applied Sciences safety risk BIM IFC schema deep excavation |
| title | Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects |
| title_full | Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects |
| title_fullStr | Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects |
| title_full_unstemmed | Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects |
| title_short | Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects |
| title_sort | semantic ifc data model for automatic safety risk identification in deep excavation projects |
| topic | safety risk BIM IFC schema deep excavation |
| url | https://www.mdpi.com/2076-3417/11/21/9958 |
| work_keys_str_mv | AT yongchengzhang semanticifcdatamodelforautomaticsafetyriskidentificationindeepexcavationprojects AT xuejiaoxing semanticifcdatamodelforautomaticsafetyriskidentificationindeepexcavationprojects AT maxwellfordjourantwiafari semanticifcdatamodelforautomaticsafetyriskidentificationindeepexcavationprojects |