Identification of nonlinear aerodynamic damping from stochastic crosswind response of tall buildings using unscented Kalman filter technique

Abstract

This study presents a new approach for identification of aerodynamic damping of tall buildings from stochastic crosswind response time history using unscented Kalman filter (UKF) technique. The system damping ratio is expressed as a polynomial function of building displacement or velocity, which is equivalent to a polynomial function of amplitude of harmonic motion. The structural system with nonlinear system damping under stochastic excitation is modeled as a single degree of freedom system with a white noise input. The augmented state variables of the system, which also include unknown system frequency, damping and stochastic excitation parameters, are estimated simultaneously with the UKF technique from the response measurement data. The aerodynamic damping is then extracted from the system damping by subtracting the structural damping. Firstly, the stochastic response time histories of a tall building model at various wind speeds with a known nonlinear aerodynamic model are simulated and the performance of the UKF technique and the influence of selections of various parameters involved are investigated. Secondly, the aerodynamic damping of a square-shaped tall building is identified based on aeroelastic building model wind tunnel test data. The response statistics are then computed using the identified damping and compared with the measurement data to verify the accuracy of identification.