Risk assessment model using conditional probability and simulation: case study in a piped gas supply chain in Brazil

2020 ◽  
pp. 1-17
Author(s):  
Liane Marcia Freitas Silva ◽  
Ana Camila Rodrigues de Oliveira ◽  
Maria Silene Alexandre Leite ◽  
Fernando A. S. Marins
Keyword(s):  
Risk Assessment ◽  
Supply Chain ◽  
Conditional Probability ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Gas Supply

Dynamic safety prewarning mechanism of human–machine–environment using computer vision

2020 ◽  
Vol 27 (8) ◽  
pp. 1813-1833 ◽  
Author(s):  
Wenpei Xu ◽  
Ting-Kwei Wang
Keyword(s):  
Risk Assessment ◽  
Computer Vision ◽  
Expert Knowledge ◽  
Safety Information ◽  
Assessment Model ◽  
Risk Indicators ◽  
Risk Assessment Model ◽  
Content Type ◽  
Risk Levels

PurposeThis study provides a safety prewarning mechanism, which includes a comprehensive risk assessment model and a safety prewarning system. The comprehensive risk assessment model is capable of assessing nine safety indicators, which can be categorised into workers’ behaviour, environment and machine-related safety indicators, and the model is embedded in the safety prewarning system. The safety prewarning system can automatically extract safety information from surveillance cameras based on computer vision, assess risks based on the embedded comprehensive risk assessment model, categorise risks into five levels and provide timely suggestions.Design/methodology/approachFirstly, the comprehensive risk assessment model is constructed by adopting grey multihierarchical analysis method. The method combines the Analytic Hierarchy Process (AHP) and the grey clustering evaluation in the grey theory. Expert knowledge, obtained through the questionnaire approach, contributes to set weights of risk indicators and evaluate risks. Secondly, a safety prewarning system is developed, including data acquisition layer, data processing layer and prewarning layer. Computer vision is applied in the system to automatically extract real-time safety information from the surveillance cameras. The safety information is then processed through the comprehensive risk assessment model and categorized into five risk levels. A case study is presented to verify the proposed mechanism.FindingsThrough a case study, the result shows that the proposed mechanism is capable of analyzing integrated human-machine-environment risk, timely categorising risks into five risk levels and providing potential suggestions.Originality/valueThe comprehensive risk assessment model is capable of assessing nine risk indicators, identifying three types of entities, workers, environment and machine on the construction site, presenting the integrated risk based on nine indicators. The proposed mechanism, which adopts expert knowledge through Building Information Modeling (BIM) safety simulation and extracts safety information based on computer vision, can perform a dynamic real-time risk analysis, categorize risks into five risk levels and provide potential suggestions to corresponding risk owners. The proposed mechanism can allow the project manager to take timely actions.

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