Non-probabilistic information fusion technique for structural damage identification based on measured dynamic data with uncertainty

Based on measured natural frequencies and acceleration responses, a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification (SMI) and two-step model updating procedure. Due to the insufficiency and uncertainty of information obtained from measurements, the uncertain problem of damage identification is addressed with interval variables in this paper. Based on the first-order Taylor series expansion, the interval bounds of the elemental stiffness parameters in undamaged and damaged models are estimated, respectively. The possibility of damage existence (PoDE) in elements is proposed as the quantitative measure of structural damage probability, which is more reasonable in the condition of insufficient measurement data. In comparison with the identification method based on a single kind of information, the SMI method will improve the accuracy in damage identification, which reflects the information fusion concept based on the non-probabilistic set. A numerical example is performed to demonstrate the feasibility and effectiveness of the proposed technique.