Multi-objective optimization-based model calibration of masonry bridges

Abstract

Multi-objective optimization-based model calibration can be an intermediate solution between the computationally expensive probabilistic approaches and the single-objective optimization strategies that do not allow uncertainty quantification of the obtained solutions. This work addresses the multi-objective model calibration of two historic stone arch bridges using high-fidelity computational FE models. To implement the methodology, a five-step approach is proposed: experimental characterization through non-destructive testing techniques, non-parametric as-built geometric modeling, macro-finite element modeling, sensitivity analysis, and multi-objective optimization. The preferred solution among the Pareto front solutions is selected based on two different classical criteria, and the set of optimal solutions is further statistically analyzed to assess the validity of the identification process. The results show an average frequency error of 0.97 % and 0.70 % and an average MAC of 0.97 and 0.96 for each case study, respectively, thus highlighting the adequacy of the proposed methodology.