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Home   /   Thesis   /   Reducing uncertainties in numerical models for civil engineering structures using Bayesian approaches

Reducing uncertainties in numerical models for civil engineering structures using Bayesian approaches


Numerical methods have been developed since years to evaluate the behavior of reinforced and prestressed structures. Even if these methods are able to simulate complex phenomena, there are still questions, especially considering the uncertainties and their impact on quantities of interest.

These uncertainties may have different origins: theoretical formulation of the model, structural complexity of the geometry or distribution of the material properties for example. In this case, uncertainty is unavoidable, especially at the structural scale. However, in the case where experimental data are available on some quantities of interest, it becomes possible to improve the estimation of these uncertainties, using data updating methods.

In this PhD thesis, the data from different experimental campaigns at the structural scale will be used to develop a methodology for uncertainty reduction. It will be based on an inverse analysis (Bayesian approaches) whose principle is to improve the estimation of probability density functions (“PDF”) associated to input parameters, using a continuous comparison between simulated outputs and experimental results.

The work will be done through a collaboration between IRSN and CEA.


Département de Modélisation des Systèmes et Structures
Service d’Etudes Mécaniques et Thermiques
Laboratoire de Mécanique Systèmes et Simulation
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