NDLI: Risk Identification Expert System for Metro Construction Based on BIM

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Risk Identification Expert System for Metro Construction Based on BIM

Content Provider	Semantic Scholar
Author	Zhang, Limao Wu, Xianguo Ding, Lieyun Chen, Yueqing Skibniewski, Miroslaw J.
Copyright Year	2013
Abstract	This paper presents a BIM-based Risk Identification Expert System (B-RIES) for metro construction, composed of three main built-in subsy stems: BIM extraction, knowledge base management, and risk identification subsystems. The engineering parameter information related to risk factors is extracted from the BIM of a specific pro ject where the IFC standard plays a bridge role between the BIM data and metro construction safety risks. An integrated knowledge base, consisting of fact base, rule base and case base, is established to systematize the fragmented explicit and tacit knowledge. A hybrid inference approach, with case-b ased reasoning and rule-based reasoning included, is developed to improve the flexibility and compreh ensiveness of the system reasoning capacity. During the safety risk identification process, B-RIES is a ble to improve the inefficiencies in engineering information extraction, reduce the dependence on do main experts, and facilitate knowledge sharing and communication among dispersed clients and domain ex perts. A typical safety hazard identification in th e Mingdu station, located in the Wuhan Metro Line Two , is presented in a case study. The results demonstrate the feasibility of B-RIES, and its appl ication potential. B-RIES can be used as a decision support tool to provide guidelines for safety manag ement in metro construction, and thus increase the likelihood of a successful project in a complex env ironment.
Starting Page	1437
Ending Page	1446
Page Count	10
File Format	PDF HTM / HTML
DOI	10.22260/ISARC2013/0163
Alternate Webpage(s)	http://www.iaarc.org/publications/fulltext/isarc2013Paper381.pdf
Alternate Webpage(s)	https://doi.org/10.22260/ISARC2013%2F0163
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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