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Bayesian Networks (BN) are efficient to represent knowledgeand for the reasoning in uncertainty. However the classic BN requiresmanual definition of the network structure by an expert, who also definesthe values entered into the conditional probability tables. In practice, it canbe time-consuming, hence the article proposes the use of LearningBayesian Networks (LBN). The aim of the study is not only to presentLBN, which can be helpful in civil engineering problems, but also toanalyze and evaluate the potential of a selected software. Based on a realexample the functional values of the Open Markov, Hugin and AgenaRiskapplications were compared.

Application possibilities of LBN for civil engineering issues