Near Real-time Inverse Analysis for Discrete Dynamic Systems using Incomplete Acceleration Time Series

摘要

From the computational point of view, parametric identification for dynamic structures presents a challenging problem because of the convergence of the inverse analysis and the limitation of available measurements. A general inverse analysis methodology for structural parameter identification by the direct use of free vibration acceleration time histories without any eigenvalue extraction process that is required in many conventional inverse analysis algorithms is proposed. An acceleration-based neural network (ANN) and a parametric evaluation neural network (PENN) are constructed to identify structural inter-storey stiffness and damping coefficients using an evaluation index called root mean square of prediction difference vector (RMSPDV). The performance of the proposed methodology is examined by numerical simulations with a multi-degree-of-freedom (MDOF) shear structure. Numerical simulation results show that the proposed methodology can be employed as a near real-time inverse analysis method for parametric identification using several seconds of spatially incomplete dynamic responses time series.