Share:


Bim-Based Risk Identification System in tunnel construction

    Limao Zhang Affiliation
    ; Xianguo Wu Affiliation
    ; Lieyun Ding Affiliation
    ; Miroslaw J. Skibniewski Affiliation
    ; Yujie Lu Affiliation

Abstract

This paper presents an innovative approach of integrating Building Information Modeling (BIM) and expert systems to address deficiencies in traditional safety risk identification process in tunnel construction. A BIM-based Risk Identification Expert System (B-RIES) composed of three main built-in subsystems: BIM extraction, knowledge base management, and risk identification subsystems, is proposed. The engineering parameter information related to risk fac­tors is first extracted from BIM of a specific project where the Industry Foundation Classes (IFC) standard plays a bridge role between the BIM data and tunnel construction safety risks. An integrated knowledge base, consisting of fact base, rule base and case base, is then established to systematize the fragmented explicit and tacit knowledge. Finally, a hybrid inference approach, with case-based reasoning and rule-based reasoning combined, is developed to improve the flexibil­ity and comprehensiveness of the system reasoning capacity. B-RIES is used to overcome low-efficiency in traditional information extraction, reduce the dependence on domain experts, and facilitate knowledge sharing and communication among dispersed clients and domain experts. The identification of a safety hazard regarding the water gushing in one metro station of China is presented in a case study. The results demonstrate the feasibility of B-RIES and its application effectiveness.

Keyword : knowledge management, tunnel construction, risk identification, decision analysis, construction safety

How to Cite
Zhang, L., Wu, X., Ding, L., Skibniewski, M. J., & Lu, Y. (2016). Bim-Based Risk Identification System in tunnel construction. Journal of Civil Engineering and Management, 22(4), 529-539. https://doi.org/10.3846/13923730.2015.1023348
Published in Issue
Aug 27, 2016
Abstract Views
3909
PDF Downloads
2449
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.