Study on Safety Risk Identification System (SRIS) for Metro Construction

2012 ◽  
Vol 452-453 ◽  
pp. 264-268
Author(s):  
Li Mao Zhang ◽  
Xian Guo Wu
2012 ◽  
Vol 27 ◽  
pp. 120-137 ◽  
Author(s):  
L.Y. Ding ◽  
H.L. Yu ◽  
Heng Li ◽  
C. Zhou ◽  
X.G. Wu ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Diandian Ding

The reasonable selection and optimized design of the deep foundation pit support scheme is directly related to the safety, construction period, and cost of the entire project. Here, based on a large number of theoretical results in many related fields, relevant influencing factors are systematically analyzed, and advanced mathematical algorithms such as neural networks are introduced according to the relevant characteristics of building deep foundation pit support construction. First of all, this paper designs and implements deep foundation pit construction safety risk technology based on wireless communication and BIM technology and analyzes and describes the framework and function of the foundation pit construction safety risk identification system. Secondly, we use neural network algorithms to study the deformation prediction of the foundation pit supporting structure, which can describe the expression method of the above safety knowledge. Finally, the differences and benefits of this method and traditional methods are compared through experiments, which show that this technology can pave the way for the construction of deep foundation pit construction safety risk knowledge.


2012 ◽  
Vol 452-453 ◽  
pp. 264-268
Author(s):  
Li Mao Zhang ◽  
Xian Guo Wu

Metro construction is gradually moving into high gear in China. However, safety incidents in metro projects have occurred frequently in recent years. Risk identification is crucial to safe construction. In order to solve problems in the current approach of risk identification, such as too much reliance on expert participation, low efficiency and so on. Safety Risk Identification System (SRIS), a combination of expert system technology and risk identification method, is researched and developed for metro construction. Through the knowledge integration process, fragmented and dispersed experiential knowledge in risk identification field on metro construction, is changed into rule base which can be directly invoked. By this way, SRIS helps realize knowledge sharing as well as risk identification automation, with important practical significance for safe construction nowadays.


2016 ◽  
Vol 28 (3) ◽  
pp. 367-374 ◽  
Author(s):  
Sara S. Webb ◽  
Karla Hemming ◽  
Madhi Y. Khalfaoui ◽  
Tine Brink Henriksen ◽  
Sara Kindberg ◽  
...  

2019 ◽  
Vol 145 (6) ◽  
pp. 04019034
Author(s):  
Sherong Zhang ◽  
Chao Shang ◽  
Chao Wang ◽  
Ran Song ◽  
Xiaohua Wang

2020 ◽  
Vol 8 (10) ◽  
pp. 5419-5425
Author(s):  
Ding‐Yan Lin ◽  
Cheng‐Han Tsai ◽  
Ying Huang ◽  
Siou‐Bang Ye ◽  
Che‐Hsuan Lin ◽  
...  

2016 ◽  
Vol 22 (4) ◽  
pp. 529-539 ◽  
Author(s):  
Limao ZHANG ◽  
Xianguo WU ◽  
Lieyun DING ◽  
Miroslaw J. SKIBNIEWSKI ◽  
Yujie LU

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.


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