Fatigue of reinforced concrete is often not considered for civil engineering structures since the self-weights of reinforced concrete structures are very high (in case of normal strength concrete) while live loads are relatively small, which leads to very small stress variations during service duration of the structure. However, particularly for bridge structures with increased use of high strength concrete and increase in traffic loads, this scenario is reversed and fatigue verification becomes much more important for the safety. This paper presents a probabilistic approach for reliability assessment of existing bridges along with reliability-based calibration of fatigue-design-factors based on the S-N approach, calibration of S-N approach with fracture-mechanics approach and reliability updating using inspections along with a case study for the Crêt de l’Anneau viaduct in Switzerland. It has been observed that, a designer needs to design the structure for fatigue life of 3.5–4.5 times the planned service life, in order to achieve the target annual reliability index of 3.7 at the end of the service life. Further, the presented framework can easily be extended to any other viaducts to estimate the fatigue reliability and maintain the safety level throughout the entire service duration.

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.