In this paper two methodologies are investigated that contribute to better assessment of risks related to extreme rainfall events. Firstly, one-parameter bivariate copulas are used to analyze rain gauge data in the Netherlands. Out of three models considered, the Gumbel copula, which indicates upper tail dependence, represents the data most accurately for all 33 stations in the Netherlands. Seasonal variability is noticeable, with rank correlation reaching maximum in winter and minimum in summer as well as other temporal and spatial patterns. Secondly, an expert judgment elicitation was undertaken. The experts’ opinions were combined using Cooke’s classical method in order to obtain estimates of future changes in precipitation patterns. Experts predicted mostly an approximate 10% increase in rain amount, duration, intensity and the dependence between amount and duration. The results were in line with official national climate change scenarios, based on numerical modelling. Applicability of both methods was presented based on an example of an existing tunnel in the Netherlands, contributing to better estimates of the tunnel’s limit state function and therefore the probability of failure.

Markov-based models for predicting deterioration for civil infrastructures are widely recognized as suitable tools addressing this mechanism. The objective of this paper is to provide insights regarding a network of orthotropic steel bridges in terms of degradation. Consequently, a model combining a dynamic Bayesian network and a Markov chain is first introduced that builds up the network in a concise way. In an attempt to represent a network composed of two general classes of orthotropic steel bridges, the classical method of structured expert judgment is carried out as a quantification procedure. The first objective is to indirectly elicit transition probabilities for a Markov chain that probabilistically describes how each type bridge temporally deteriorates. Second, experts are asked to provide estimates on required conditional probabilities related to the Bayesian network. An in-depth analysis of the results is presented so that remarks and observations are subsequently pointed out and, finally conclusions are drawn.