Investigation of Uncertainty Changes in Model Outputs for Finite-Element Model Updating Using Structural Health Monitoring Data

This article aims to investigate the effect of uncertainties on the predicted response of structures using updated finite-element models (FEMs). Modeling uncertainties are quantified by fuzzy numbers and are incorporated into the fuzzy FEM updating procedure. The impact of the amount and types of data used on the performance of the updated model is investigated. In order to perform the complex FEM updating calculations, which generally take too much time for complex models, a Gaussian process (GP) is used as a surrogate model. The central composite design (CCD) method is used to sample the input parameter space for more accurate GP models. Genetic algorithms (GA) are employed to solve the inverse fuzzy model updating problem. Additional constraints are presented to capture the variation space of the uncertain response parameters. The University of Central Florida benchmark test structure, which is designed to represent short-span to medium-span bridges, is used in the scope of uncertainty quantification study. Static and dynamic experimental test data obtained from the benchmark structure under different loadings and conditions are used for the demonstration. A damage case, in which the stiffness reduction in boundaries is simulated by using flexible pads, is considered. The results show that appropriate data sets, which contain the least uncertainty, should be generated instead of involving the entire set of measurements obtained from different tests. Nevertheless, uncertainty quantification should be employed to find the variation range of uncertain responses predicted by simplified FEM models.