Bayesian Updating of a Cracking Model for Reinforced Concrete Structures Subjected to Static and Cyclic Loadings

Abstract

Several reinforced concrete structures fail by fatigue loads. The effects of this type of loading are complex. Many mechanical models based on the damage theory could be used to represent the behavior of reinforced concrete under cyclic loading. Their use requires, among others, knowledge of the material characteristic parameters and its related uncertainties that could be determined from experimental tests. However, the models are time-consuming and the experimental data scarce. In this chapter, we propose a Bayesian network–based methodology to propagate uncertainties in the damage theory model. The proposed methodology is useful to identify the uncertainties of the damage model used when some parameters are measured. The methodology is illustrated with a reinforced concrete beam subjected to cyclic loading. The results obtained were compared with those of the experimental tests to validate the proposed methodology. The good agreement indicates that our approach is capable of propagating uncertainties and integrating data from experimental tests. The proposed approach could be also used to identify the uncertainties of the model used by introducing experimental measurements.