Knowledge and Data Integration for Modeling of Problems of Risk due to Natural and Man-Made Hazards

Durgaprasad, J.; Raju, P. S. N.

doi:10.1061/(asce)hz.1944-8376.0000037

Cited by 2 publications

(2 citation statements)

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“…Based on the Bayesian theorem, the Bayesian approach enables updating probabilities when new data are introduced. The reliability of the output produced by the Bayesian approach can be reflected by a number of analyses, such as the sensitivity of a particular estimate on the model's output and measures that reflect the extent to which judgmental estimates might change in the face of new evidence …”

Section: Standard Bbns Overviewmentioning

confidence: 99%

“…(29) The reliability of the output produced by the Bayesian approach can be reflected by a number of analyses, such as the sensitivity of a particular estimate on the model's output and measures that reflect the extent to which judgmental estimates might change in the face of new evidence. (30) In a BBN, the interrelationships between variables are expressed graphically in the form of diagrams. Variables are represented by nodes.…”

Section: Standard Bbns Overviewmentioning

confidence: 99%

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Modeling Risk‐Related Knowledge in Tunneling Projects

et al. 2013

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Knowledge on failure events and their associated factors, gained from past construction projects, is regarded as potentially extremely useful in risk management. However, a number of circumstances are constraining its wider use. Such knowledge is usually scarce, seldom documented, and even unavailable when it is required. Further, there exists a lack of proven methods to integrate and analyze it in a cost-effective way. This article addresses possible options to overcome these difficulties. Focusing on limited but critical potential failure events, the article demonstrates how knowledge on a number of important potential failure events in tunnel works can be integrated. The problem of unavailable or incomplete information was addressed by gathering judgments from a group of experts. The elicited expert knowledge consisted of failure scenarios and associated probabilistic information. This information was integrated using Bayesian belief-networks-based models that were first customized in order to deal with the expected divergence in judgments caused by epistemic uncertainty of risks. The work described in the article shows that the developed models that integrate risk-related knowledge provide guidance as to the use of specific remedial measures.

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Section: Standard Bbns Overviewmentioning

confidence: 99%

Section: Standard Bbns Overviewmentioning

confidence: 99%