scholarly journals Study on risk assessment of expressway nighttime maintenance construction: A Dynamic Bayesian Network model

2021 ◽  
Vol 257 ◽  
pp. 02047
Author(s):  
Zhen Tian ◽  
Jinhua Fan ◽  
Qianqian Chen ◽  
Huaichen Hu ◽  
Yanyang Shen
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Bayesian Network ◽  
Traffic Accidents ◽  
Dynamic Bayesian Network ◽  
Extended Model ◽  
Time Varying ◽  
Bayesian Network Model ◽  
Change Curve ◽  
Or Gate

There are many risk factors and large uncertainties in expressway nighttime maintenance construction(ENMC), and the state of risk factors will change dynamically with time. In this study, a Dynamic Bayesian Network (DBN) model was proposed to investigate the dynamic characteristics of the time-varying probability of traffic accidents during expressway maintenance at night. Combined with Leaky Noisy-or gate extended model, the calculation method of conditional probability is determined . By setting evidences for DBN reasoning, the time series change curve of the probability of traffic accidents and other risk factors are obtained. The results show that DBN can be applied to risk assessment of ENMC.

scholarly journals Risk Analysis of Earth-Rock Dam Breach Based on Dynamic Bayesian Network

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2305 ◽  
Author(s):  
Li ◽  
Wang ◽  
Ge ◽  
Wei ◽  
Li
Keyword(s):  
Risk Analysis ◽  
Bayesian Network ◽  
Dynamic Bayesian Network ◽  
Time Factors ◽  
Extended Model ◽  
Bayesian Network Model ◽  
Dam Breach ◽  
The Earth ◽  
Operation Period ◽  
Static Risk

Despite the fact that the Bayesian network has great advantages in logical reasoning and calculation compared with the other traditional risk analysis methods, there are still obvious shortcomings in the study of dynamic risk. The risk factors of the earth-rock dam breach are complex, which vary with time during the operation period. Static risk analysis, limited to a specific period of time, cannot meet the needs of comprehensive assessment and early warning. By introducing time factors, a dynamic Bayesian network model was established to study the dynamic characteristics of dam-breach probability. Combined with the calculation of the conditional probability of nodes based on the Leaky Noisy-Or gate extended model, the reasoning results of Bayesian networks were modified by updating the data of different time nodes. Taking an earth-rock dam as an example, the results show that it has less possibility to breach and keep stable along the time axis. Moreover, the factors with vulnerability and instability were found effective, which could provide guidance for dam risk management.

scholarly journals A Bayesian network model for personalised COVID19 risk assessment and contact tracing

2020 ◽  
Author(s):  
Norman E Fenton ◽  
Scott McLachlan ◽  
Peter Lucas ◽  
Kudakwashe Dube ◽  
Graham A Hitman ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Bayesian Network ◽  
Social Interactions ◽  
Probabilistic Model ◽  
Personal Information ◽  
Contact Tracing ◽  
Smartphone App ◽  
Bayesian Network Model ◽  
The Uk

AbstractConcerns about the practicality and effectiveness of using Contact Tracing Apps (CTA) to reduce the spread of COVID19 have been well documented and, in the UK, led to the abandonment of the NHS CTA shortly after its release in May 2020. We present a causal probabilistic model (a Bayesian network) that provides the basis for a practical CTA solution that addresses some of the concerns and which has the advantage of minimal infringement of privacy. Users of the model can provide as much or little personal information as they wish about relevant risk factors, symptoms, and recent social interactions. The model then provides them feedback about the likelihood of the presence of asymptotic, mild or severe COVID19 (past, present and projected). When the model is embedded in a smartphone app, it can be used to detect new outbreaks in a monitored population and identify outbreak locations as early as possible. For this purpose, the only data needed to be centrally collected is the probability the user has COVID19 and the GPS location.

scholarly journals Time-varying dynamic Bayesian network model and its application to brain connectivity using electrocorticograph

2016 ◽  
Vol 65 (3) ◽  
pp. 038702
Author(s):  
Guo Miao-Miao ◽  
Wang Yu-Jing ◽  
Xu Gui-Zhi ◽  
Griffin Milsap ◽  
Nitish V. Thakor ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Brain Connectivity ◽  
Dynamic Bayesian Network ◽  
Time Varying ◽  
Bayesian Network Model

Dynamic Bayesian Network Model of a Country’s Economic Extension

2020 ◽  
pp. 145-159
Author(s):  
Mariia Voronenko ◽  
Dmytro Nikytenko ◽  
Jan Krejci ◽  
Nataliia Krugla ◽  
Oleksandr Naumov ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Dynamic Bayesian Network ◽  
Bayesian Network Model

Bayesian mutual information reliability model for fire risk assessment of high-rise buildings

2020 ◽  
pp. 002072091989419
Author(s):  
Jingjing Pei ◽  
Guantao Wang
Keyword(s):  
Risk Assessment ◽  
Bayesian Network ◽  
Quantitative Assessment ◽  
Fire Risk ◽  
Event Tree ◽  
Bayesian Network Model ◽  
Tree Model ◽  
High Rise ◽  
Fire Risk Assessment ◽  
Information Reliability

The Bayesian network method is introduced into the process of fire risk quantitative assessment. The event tree model is established, and the Bayesian network model is transformed from the event tree model based on the typical fire scenarios in high-rise space. A Bayesian fire risk assessment algorithm for high-rise buildings based on mutual information reliability is proposed. Bayesian network is modified considering the influence of uncertainties. Finally, the modified Bayesian network model is used to calculate the probability of fire developing to different stages, and the estimated value of property loss is used to express the severity of the accident and calculate the fire risk value. The results show that the existence of uncertainties has a significant impact on the results of risk assessment; the quantitative assessment method based on Bayesian network is better than the ETA method based on event tree analysis in dealing with uncertainties and is more suitable for high-rise space fire risk assessment.

An Artificial Neural Network and Bayesian Network model for liquidity risk assessment in banking

2018 ◽  
Vol 275 ◽  
pp. 2525-2554 ◽  
Author(s):  
Madjid Tavana ◽  
Amir-Reza Abtahi ◽  
Debora Di Caprio ◽  
Maryam Poortarigh
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Artificial Neural Network ◽  
Bayesian Network ◽  
Network Model ◽  
Liquidity Risk ◽  
Bayesian Network Model ◽  
Artificial Neural
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