Parameter Selection in Finite-Element-Model Updating by Global Sensitivity Analysis Using Gaussian Process Metamodel

Abstract

AbstractParameter selection is a key step in finite-element-model updating (FEMU) because it determines whether the task of FEMU is successful or not. The as-built engineering structures are inevitably subject to many sources of uncertainty such as geometric dimension variability due to manufacture process, inherent random variation of materials, and imprecisely known boundary conditions. Uncertainty involving parameters challenges the task of parameter selection in FEMU. In this paper, the powerful global sensitivity analysis (GSA) is proposed to perform parameter selection in FEMU when uncertainty exists. The Monte Carlo simulation (MCS) method is extensively adopted to perform GSA. However, the brute-force MCS method is likely to be unaffordable and impractical because it entails a large number of model evaluations due to its slow convergence. Therefore, the Gaussian process metamodel is used as the surrogate model of the time-consuming finite-element model to ease the heavy computational burden. Gauss...