Nonparametric nonlinear restoring force and excitation identification with Legendre polynomial model and data fusion

Identification of nonlinear restoring force and dynamic loadings provides critical information for post-event damage diagnosis structures. Due to high complexity individuality structural nonlinearities, it is difficult provide an exact parametric mathematical model in advance describe the behavior a member or substructure under strong practice. Moreover, external loading applied engineering structure usually unknown only acceleration responses at limited degrees freedom are available identification. In this study, nonparametric excitation identification approach combining Legendre polynomial extended Kalman filter with input proposed using measurements fused displacement measurements. Then, performance first illustrated via numerical simulation multi-degree-of-freedom frame structures equipped magnetorheological dampers mimicking nonlinearity direct base noise-polluted Finally, experimental study on four-story steel damper carried out response measurement employed validate effectiveness method by comparing identified responses, force, test The convergence effect initial estimation errors parameters final results investigated. data fusion improving accuracy also