Model updating of a historic concrete bridge by sensitivity- and global optimization-based Latin Hypercube Sampling

In this paper, a self-implemented model updating global optimization procedure is successfully applied to a remarkable case study concerning a historic centennial Reinforced Concrete (RC) bridge with parabolic arches, based on recorded experimental vibrational data and arising identification of modal properties. In order to boost the degree of confidence and robustness of the developed model updating procedure, appropriate computational strategies are proposed at the level of both Sensitivity Analysis (SA) and global optimization. In particular, Latin Hypercube Sampling (LHS) is employed in drawing up both strategies, as a systematic automated way to determine appropriate multi-start sets of initiation points, optimally distributed throughout the parametric domain. The procedure involves a gradient-based method and proposes an interaction algorithm between mechanical FEM solver and numerical computing environment. Moreover, the gradient of the objective function involved in the model updating is analytically derived, instead of by often-used Finite Differences (FD), toward better accuracy and computational efficiency. Comprehensive updating results starting from a first FEM base model are achieved, for the considered case study, and show that the relative eigenfrequency and mode shape estimations are considerably improved, for all the structural modes accounted for within the updating process, with a very good final matching between experimentally extracted and FEM modelled modal properties.