Bayesian inference with correction of model bias for Thermo-Hydro-Mechanical models of large concrete structures. (1st March 2023) - Rossat, D.; Baroth, J.; Briffaut, M.; Dufour, F.; Monteil, A.; Masson, B.; Michel-Ponnelle, S.
Abstract: Drying and creep are mainly driving the continuous strain evolution in time of aging large prestressed concrete structures, and may jeopardize the safety of their environment. Therefore, it is crucial to dispose of an accurate assessment of structures' long-term strain level so as to evaluate their integrity. In the framework of probabilistic modeling of the delayed mechanical analysis of aging concrete structures, Bayesian inference allows to update uncertainties related to input parameters of computational models through the assimilation of noisy monitoring data. In this paper, a Bayesian inference methodology aiming at updating uncertain parameters of computational models for large concrete structures is proposed. This methodology combines a powerful surrogate modeling technique named PC-PCE ( Principal Component Polynomial Chaos Expansions ) with the so-called BUS (Bayesian Updating with Structural reliability methods) framework, in order to efficiently draw samples from posterior distributions for a sensibly reduced computational cost. In particular, the methodology is based on a framework to account for model uncertainties and biases, which are usually disregarded in the existing literature related to large concrete structures, and it enables correction predictions through Bayesian inference. The proposed approach is illustrated through an application to a large aging prestressed concrete structure, namely a 1:3 scale mock-up of a Nuclear Containment Building. In this context, a thermo-hydro-mechanical computational model with uncertain parameters is adopted to model the time evolution of strains of the structure. Results emphasize that the proposed approach performs Bayesian updating for a reduced computational cost. The proposed Bayesian inference approach also enables the identification of model biases, and correction of strain predictions. Highlights: Bayesian inference with correction of model bias and global variance-based sensitivity analysis for aging large containment structures. Bayesian computations are performed with the Subset Simulation (SuS) algorithm. Principal component polynomial chaos expansions are used to accelerate the SuS algorithm and computing Sobol' sensitivity indices. A case study of a 1:3 scale nuclear containment building is presented.