Abstract
This paper presents recent contributions to the Marie Skłodowska-Curie Innovative Training Network titled INFRASTAR (Innovation and Networking for Fatigue and Reliability Analysis of Structures-Training for Assessment of Risk) in the field of reliability approaches for decision-making for wind turbines and bridges . Stochastic modeling of uncertainties for fatigue strength parameters is an important step as a basis for reliability analyses. In this paper, the Maximum Likelihood Method (MLM) is used for fitting the statistical parameters in a regression model for the fatigue strength of reinforcement bars. Furthermore, application of the Bootstrapping method is investigated. The results indicate that the latter methodology does not work well in the considered case study because of run-out tests within the test data. Moreover, the use of the Bayesian inference with the Markov Chain Monto Carlo approach is studied. These results indicate that a reduction in the statistical uncertainty can be obtained, and thus, better parameter estimates are obtained. The results are used for stochastic modelling in reliability assessment of a case study with a composite bridge. The reduction in statistical uncertainty shows high impact on the fatigue reliability in a case study on the Swiss viaduct Crêt De l’Anneau.
Highlights
This paper presents statistical analyses performed on fatigue data obtained from [1], where laboratory fatigue tests were performed on reinforcement bars
Statistical analyses of the data are an essential step for the stochastic modeling of the material fatigue uncertainties, which can be used as a basis for a probabilistic modeling and reliability analysis [19]
This includes results obtained for the statistical parameters by Maximum Likelihood Method (MLM) accounting for run-outs
Summary
This paper presents statistical analyses performed on fatigue data obtained from [1], where laboratory fatigue tests were performed on reinforcement bars (rebars).General methods and techniques utilized for risk and reliability assessment of civil engineering structures are presented [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18].Statistical analyses of the data are an essential step for the stochastic modeling of the material fatigue uncertainties, which can be used as a basis for a probabilistic modeling and reliability analysis [19]of structures with reinforced concrete components, such as wind turbines and bridges [20,21]. This paper presents statistical analyses performed on fatigue data obtained from [1], where laboratory fatigue tests were performed on reinforcement bars (rebars). Statistical analyses of the data are an essential step for the stochastic modeling of the material fatigue uncertainties, which can be used as a basis for a probabilistic modeling and reliability analysis [19]. Of structures with reinforced concrete components, such as wind turbines and bridges [20,21]. The development of stochastic models for the fatigue limit state and estimation of the resulting reliability can be considered as a contribution to reliability assessment of these types of structures, with respect to fatigue failure and as the basis for the development of optimal strategies for the maintenance of wind turbines and bridges.
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