Efficient Inverse Method for Structural Identification Considering Modeling and Response Uncertainties

Abstract The inverse problem analysis method provides an effective way for the structural parameter identification. However, uncertainties wildly exist in practical engineering problems. Due to coupling of multi-source measured responses and modeling parameters, traditional under deterministic framework faces challenges solving mechanism computing cost. In this paper, uncertain based on convex model dimension reduction decomposition is proposed realize interval identification unknown parameters according parameters. Firstly, polygonal set established quantify epistemic Afterwards, a space collocation transform considering into few problems response uncertainty. transformed involves two-layer process including propagation optimization updating. order solve uncertainty, efficient high dimensional representation affine algorithm further developed. Through above two strategies, avoids time-consuming multi-layer nested calculation procedure, then effectively realizes uncertainty Finally, examples are provided verify effectiveness method.