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We combine Bayesian networks (BNs) and structural reliability methods (SRMs) to create a new computational framework, termed enhanced Bayesian network (eBN), for reliability and risk analysis of engineering structures and infrastructure. BNs are efficient in representing and evaluating complex probabilistic dependence structures, as present in(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)
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)
The present article starts out by proposing a framework for risk assessment of RC structures utilizing condition indicators. Thereafter, the various building stones of the suggested framework are described. This description includes a summary of the basis for the prob-abilistic modeling of the initiation phases of chloride-induced corrosion of concrete(More)
A new system for estimating the state of roads during flooding based on probabilistic graphical models is presented. The location of the roads is given by a geographic information system whereas the up-to-date information for the assessment of flood state is delivered by remote sensing data. Furthermore, the height information from a digital elevation model(More)