A surrogate-based approach to accelerate the design and build phases of reinforced concrete bridges

Integrating uncertainties in the design process of reinforced concrete rail bridges, in a fully probabilistic framework, makes their design more complex and challenging. To propagate these uncertainties and convey their influence on the performance of the engineering system, a high-dimensional design space is supposed to be explored. A great challenge to be considered here lies in the computational burden as conducting such an exploration campaign requires substantial calls to computationally expensive finite element simulations. To address this challenge, a surrogate model mapping the design space to the reinforced concrete bridge performance functions is developed in the context of an active learning algorithm. The importance of this model lies in its ability to explore as many design scenarios as possible with minimal computational resources and classify the design scenarios into failure and safe scenarios. This work considers a 4-span reinforced concrete bridge deck. A multi-fiber finite element model of this beam is developed in Cast3m to generate the required design of experiments for the surrogate model. A performance comparison is undertaken to evaluate the Kriging surrogate model effectiveness with and without active learning while the reliability of Kriging predictions is also assessed in comparison to PC-Kriging.