Development of Stochastic Fatigue Model of Reinforcement for Reliability of Concrete Structures
Source abstract
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.
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Research context
OpenAlex topics: Probabilistic and Robust Engineering Design · Structural Health Monitoring Techniques · Concrete Corrosion and Durability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Civil Engineering, Aalborg University, 9100 Aalborg, Denmark
Department of Mechanical Engineering, Technical University of Denmark, 2800 Lyngby, Denmark
Funding
European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement
References (48)
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