Using Reduced Basis Approximation for Efficient Surrogate-based Inverse Identification of Thermo-Hydro-Mechanical Parameters from an In-situ Heating Test

In this paper, a reduced order model built from the reduced basis (RB) method is used as a surrogate for an inverse identification problem related to coupled thermo-hydro-mechanical (THM) processes. The RB space - spanned by solutions of the governing THM equations - is constructed using a greedy adaptive procedure guided by an a-posteriori error estimator that selects the optimal snapshot points in a given parametric space. The RB model is assessed in terms of accuracy and computational cost reduction for the three-dimensional transient coupled problem described by the ATLAS III small scale in-situ heating test. The substantial system size reduction and the associated significant computational gain result in a surrogate model suitable for parameter identification procedures in the Boom Clay material. The effectiveness of the proposed strategy is demonstrated by performing inverse analysis based on direct-search and genetic algorithm (GA) optimization supported by real sensor measurement data where 800 times faster computational speed-up was achieved.