Forensic assessment of a bridge downfall using Bayesian networks

Holický, Milan; Marková, Jana; Sýkora, Milan

doi:10.1016/j.engfailanal.2012.12.014

Cited by 35 publications

(17 citation statements)

References 8 publications

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“…A BBN uses a directed acyclic graph (DAG)15 to represent the variables and probabilistic causal dependence among the variables. Each variable in the BBN is presented as a node with directed links.…”

Section: Methodsmentioning

confidence: 99%

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A new method for assessing the risk of infectious disease outbreak

Liao

Wang

et al. 2017

Sci Rep

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Over the past few years, emergent threats posed by infectious diseases and bioterrorism have become public health concerns that have increased the need for prompt disease outbreak warnings. In most of the existing disease surveillance systems, disease outbreak risk is assessed by the detection of disease outbreaks. However, this is a retrospective approach that impacts the timeliness of the warning. Some disease surveillance systems can predict the probabilities of infectious disease outbreaks in advance by determining the relationship between a disease outbreak and the risk factors. However, this process depends on the availability of risk factor data. In this article, we propose a Bayesian belief network (BBN) method to assess disease outbreak risks at different spatial scales based on cases or virus detection rates. Our experimental results show that this method is more accurate than traditional methods and can make uncertainty estimates, even when some data are unavailable.

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Section: Methodsmentioning

confidence: 99%

“…They are graphical models of the probabilistic relationships among a set of variables that can express the mutual dependencies between variables in terms of quality and quantity15. As such, BBNs have been used in many fields because of their simplicity and soundness, especially in medical and industrial diagnosis16.…”

mentioning

confidence: 99%

A new method for assessing the risk of infectious disease outbreak

Liao

Wang

et al. 2017

Sci Rep

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“…A Bayesian belief network (BBN) is a graphical model for probabilistic relationships among a set of variables; it can express the mutual dependencies between variables in terms of quality and quantity, and it provides a good explanation for knowledge representation and reasoning (Holický et al 2013). This method can also produce uncertainty estimates even with missing values.…”

Section: Introductionmentioning

confidence: 99%

Using a Bayesian belief network model for early warning of death and severe risk of HFMD in Hunan province, China

Liao

Liu

et al. 2018

Stoch Environ Res Risk Assess

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Hand, foot, and mouth disease (HFMD) is a global infectious disease resulting in millions of cases and even hundreds of deaths. Although a newly developed formalin-inactivated EV71 (FI-EV71) vaccine is effective against EV71, which is a major pathogen for HFMD, no vaccine against HFMD itself has yet been developed. Therefore, establishing a sensitive and accurate early warning system for HFMD is important. The early warning model for HFMD in the China Infectious Disease Automated-alert and Response System combines control chart and spatial statistics models to detect spatiotemporal abnormal aggregations of morbidity. However, that type of early warning for HFMD just involves retrospective analysis. In this study, we apply a Bayesian belief network (BBN) to estimate the increased risk of death and severe HFMD in the next month based on pathogen detection and environmental factors. Hunan province, one of the regions with the highest prevalence of HFMD in China, was selected as the study area. The results showed that compared with the traditional early warning model for HFMD, the proposed method can achieve a very high performance evaluation (the average AUC tests were more than 0.92). The model is also simple and easy to operate. Once the structure of the BBN is established, the increased risk of death and severe HFMD in the next month can be estimated based on any one node in the BBN. Keywords Hand, foot, and mouth disease Á Early warning Á Spatiotemporal abnormal aggregation Á Moving percentile method Á Bayesian belief network

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“…As a common approach to quantitatively analyze risk occurrence probability, Bayesian network has been widely used in construction projects to study slope collapse (Cheng and Hoang, ), fall risk of cantilever bridge projects (Chen and Leu, ), deep water drilling operations (Bhandari et al., ), etc., and has also been applied to other diverse research fields (Sun and Bette, ; Wang et al., ; Yuen and Mu, ; Mu and Yuen, ). It not only combines the merits of fault tree and other risk probability quantitative analysis methods, but also has distinct advantages in describing dependence between variables quantitatively (Holický et al., ), and dealing with uncertainty information (Lee et al., ). These merits make Bayesian network a more suitable tool in analyzing occurrence probability.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Risk Analysis of Diversion Tunnel Construction Simulation

Zhong

Ren

et al. 2017

Computer aided Civil Eng

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Comprehensive and effective risk analysis is significant for studying construction simulation of diversion tunnel. Existing tunnel risk simulation approaches mainly analyze ordinary risk factors, and cannot quantitatively study risk events considering their causes. Additionally, in other tunnel probabilistic risk analysis methods, although some studies have made full probabilistic estimates of tunnel schedule, risk factors are unable to be studied considering cyclic construction characteristics and occurrence probability of risk events cannot be determined quantitatively. To address the issues, a probabilistic risk analysis approach of diversion tunnel construction simulation is proposed. Based on hierarchical simulation model, risk factors can be analyzed at operation level of tunnel construction. Moreover, Bayesian network is embedded into simulation program to quantitatively calculate probability of risk events in each simulation cycle, considering geology, design, construction, and management conditions and their mutual dependence.C 2017 Computer-Aided Civil and Infrastructure Engineering.

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Forensic assessment of a bridge downfall using Bayesian networks

Cited by 35 publications

References 8 publications

A new method for assessing the risk of infectious disease outbreak

A new method for assessing the risk of infectious disease outbreak

Using a Bayesian belief network model for early warning of death and severe risk of HFMD in Hunan province, China

Probabilistic Risk Analysis of Diversion Tunnel Construction Simulation

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