Daniel Straub

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Avalanche disasters are associated with significant monetary losses. It is thus crucial that avalanche risk assessments are based on a consistent and proper assessment of the uncertainties involved in the modelling of the avalanche runout zones and the estimations of the damage potential. We link a Bayesian network (BN) to a Geographic Information System(More)
The objective of the present paper is the demonstration of the potential and advantages of Bayesian networks for the application in risk assessments for natural hazards. For this purpose, a general framework for natural hazards risk assessment is presented and a brief introduction to Bayesian networks is provided. The methodology is then applied to rating(More)
A Dynamic Bayesian Network (DBN) model for probabilistic assessment of tunnel construction performance is introduced. It facilitates the quantification of uncertainties in the construction process and of the risk from extraordinary events that cause severe delays and damages. Stochastic dependencies resulting from the influence of human factors and other(More)
Cost-benefit analysis (CBA) is commonly applied as a tool for deciding on risk protection. With CBA, one can identify risk mitigation strategies that lead to an optimal tradeoff between the costs of the mitigation measures and the achieved risk reduction. In practical applications of CBA, the strategies are typically evaluated through efficiency indicators(More)
The Bayesian network (BN) is a convenient tool for probabilistic modeling of system performance, particularly when it is of interest to update the reliability of the system or its components in light of observed information. In this paper, BN structures for modeling the performance of systems that are defined in terms of their minimum link or cut sets are(More)