Safety Risk Assessment of Tower Crane Construction Based on Fuzzy Bayesian Network

2021 ◽  
Author(s):  
Lin Liu ◽  
Jie Liu ◽  
Yining Yao
Keyword(s):  
Risk Assessment ◽  
Bayesian Network ◽  
Safety Risk ◽  
Tower Crane

scholarly journals RISK ASSESSMENT IN 13-STORY BUILDING PROJECTS WITH THE FAILURE MODE AND EFFECT ANALYSIS (FMEA) METHOD AND THE DOMINO EFFECT

2021 ◽  
Vol 6 (2) ◽  
pp. 83-95
Author(s):  
Salman Farid Lahmadi ◽  
Betanti Ridhosari ◽  
I Wayan Koko Suryawan ◽  
Ariyanti Sarwono
Keyword(s):  
Risk Assessment ◽  
Failure Mode ◽  
Project Work ◽  
Effect Analysis ◽  
Building Projects ◽  
Safety Risk ◽  
Tower Crane ◽  
Work Area ◽  
Fmea Method

The building construction project is one of the activities that can pose a safety risk. Work safety risk assessment can be done using the Failure Mode and Effect Analysis (FMEA) method and looking at the Risk Priority Number (RPN) value. The purpose of this research is to take a case study of the building Office in determining the highest RPN and provide recommendations on its management. This project consists of 13 earthworks, passenger hoist, tower crane, scaffolding jobs, ironworks, formwork work, foundry work, mechanical, electrical plumbing (MEP) work, welding work, and floor wall doing works, and ceramic installation work. The highest RPN from the observations occurred in Iron Fabrication which can cause fingers hit by a bar cutter and bender. In this case, personal protective equipment (PPE) is significant in preventing these impacts from occurring in the project work area.

scholarly journals Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method

2019 ◽  
Vol 16 (7) ◽  
pp. 1166 ◽  
Author(s):  
Tianpei Tang ◽  
Senlai Zhu ◽  
Yuntao Guo ◽  
Xizhao Zhou ◽  
Yang Cao
Keyword(s):  
Bayesian Network ◽  
Low Cost ◽  
Road Networks ◽  
Access Point ◽  
Safety Risk ◽  
Crash Data ◽  
Risk Levels ◽  
The Road ◽  
Run Off ◽  
Point Density

Evaluating the safety risk of rural roadsides is critical for achieving reasonable allocation of a limited budget and avoiding excessive installation of safety facilities. To assess the safety risk of rural roadsides when the crash data are unavailable or missing, this study proposed a Bayesian Network (BN) method that uses the experts’ judgments on the conditional probability of different safety risk factors to evaluate the safety risk of rural roadsides. Eight factors were considered, including seven factors identified in the literature and a new factor named access point density. To validate the effectiveness of the proposed method, a case study was conducted using 19.42 km long road networks in the rural area of Nantong, China. By comparing the results of the proposed method and run-off-road (ROR) crash data from 2015–2016 in the study area, the road segments with higher safety risk levels identified by the proposed method were found to be statistically significantly correlated with higher crash severity based on the crash data. In addition, by comparing the respective results evaluated by eight factors and seven factors (a new factor removed), we also found that access point density significantly contributed to the safety risk of rural roadsides. These results show that the proposed method can be considered as a low-cost solution to evaluating the safety risk of rural roadsides with relatively high accuracy, especially for areas with large rural road networks and incomplete ROR crash data due to budget limitation, human errors, negligence, or inconsistent crash recordings.

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