A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk This article presents a case study involving the assessment of an existing bridge, starting with simple methods and ending with a probabilistic analysis, the latter emphasizing Bayesian methods. When assessing an existing bridge, it is common practice to collect information from the bridge in the form of samples. These samples are in general of small size, raising the question of how the corresponding statistical uncertainty can be taken into account on reliability estimates. The case study illustrates how Bayesian methods are especially suitable to deal with that source of uncertainty. Another strong point of the Bayesian methods is their ability to combine the information contained in the samples collected from the bridge with prior information, if any. This aspect will also be illustrated through the case study.November 29, 2014 1:41 Structure and Infrastructure Engineering jacinto14 Structure and Infrastructure Engineering, Vol. 00, No. 00, Month 200x, 1-
This study focus on the probabilistic modelling of mechanical properties of prestressing strands based on data collected from tensile tests carried out in Laboratório Nacional de Engenharia Civil (LNEC), Portugal, for certification purposes, and covers a period of about 9 years of production. The strands studied were produced by 6 manufacturers from 4 countries, namely Portugal, Spain, Italy and Thailand. Variability of the most important mechanical properties is examined and the results are compared with the recommendations of the Probabilistic Model Code, as well as the Eurocodes and earlier studies. The obtained results show a very low variability which, of course, benefits structural safety. Based on those results, probabilistic models for the most important mechanical properties of prestressing strands are proposed.
born 1967, received his civil engineering degree from the Technical University of Lisbon in 1990 and collaborated in several consultant firms. Presently he is preparing his doctoral thesis.
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