Yan Fu Wang
China University of Petroleum
30 Papers
134 Citations
Yan Fu Wang is an academic researcher from China University of Petroleum. The author has contributed to research in topics: Bayesian network & Computer science. The author has an hindex of 14, co-authored 24 publications. Previous affiliations of Yan Fu Wang include National University of Singapore & Liverpool John Moores University.
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Papers
Full-scale experiment research and theoretical study for fires in tunnels with roof openings
TL;DR: In this paper, full-scale fire experiments in tunnels with roof openings are carried out, which were rarely reported in the previous references, in order to assess the possibility of exhausting smoke through passive roof openings and the influence of smoke on personnel in the tunnels.
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Investigations of Human and Organizational Factors in hazardous vapor accidents
TL;DR: Case studies show that the model is capable of seeking out critical latent human and organizational errors and carrying out quantitative analysis of accidents and corresponding safety prevention measures are derived.
71
Modelling ship collision risk based on the statistical analysis of historical data: A case study in Hong Kong waters
TL;DR: A new framework of quantitative risk assessment is proposed by quantifying the probability and the corresponding consequence based on the historical accident data and can be used to assess present risks to plan preventive measures for the potential collision accidents.
53
Simulation of back-layering length in tunnel fire with vertical shafts
TL;DR: In this paper, a theoretical analysis is carried out to predict the back-layering length of fire in the tunnel with vertical shafts, and the calculation results from the proposed model are compared with both experimental data and simulation results.
50
Accident analysis model based on Bayesian Network and Evidential Reasoning approach
Yan Fu Wang,Min Xie,Min Xie,Kwai-Sang Chin,Xiu Ju Fu +4 more
- 14 Mar 2013
TL;DR: In this article, an accident analysis model is proposed to develop the cost-efficient safety measures for preventing accidents, which can be used to seek out accident causes and rank the derived safety measures from a cost-effectiveness perspective.
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