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Abstract: Many theoretical methods for evaluating robustness have been developed in the last decades. Especially probability‐based robustness quantification promises to provide a rational basis for decision‐making. To allow for this type of robustness evaluation, the probability of failure (or the corresponding reliability index) of the structural system in the damaged and the undamaged state has to be determined. In this paper, the Probability Density Evolution Method (PDEM) is used to evaluate the dynamic reliability, taking into account the randomness which comes both from the excitation and from the stochastic system properties. The efficiency of the PDEM for robustness quantification is evaluated through a simple cargo crane example case. In addition, the instant equivalent extreme‐value event, which is quite different from the equivalent extreme‐value event for one variable, is applied to obtain the extreme value of reaction forces for each sample at instant time. [ABSTRACT FROM AUTHOR]

Abstract: Climate change could significantly affect climatic actions, so influencing not only existing structures, designed in accordance to the provisions of past Codes, but also updating of structural Codes. In the paper a new probabilistic technique for bias correction and downscaling of climate projections is presented to assess uncertainties in the future trends of climate extremes at the local scale. Referring to the relevant historical period, climate model outputs at relevant weather stations are compared with recorded daily series of maximum and minimum temperatures and water precipitations to derive appropriate monthly error probability density functions. Subsequently, new climate data series are generated by adding to the climate models output a random error term sampled from the monthly error PDFs. An extreme value analysis is finally carried out for each generated series according different time windows to assess how characteristic values (50 years return period) vary over time. The results for snow loads at the investigated weather stations confirm that the proposed methodology is suitable to reproduce recorded past climate extremes, so suggesting that it could be a useful tool to improve the climate change forecasts derived from climate models. [ABSTRACT FROM AUTHOR]