Improved Gaussian Mixed Particle Filter Dynamic Prediction of Bridge Monitored Extreme Stress

Abstract:

To reasonably and dynamically predict the extreme stress information of inservice bridge, in this paper, the nonlinear dynamic models were built including monitoring equation and state equation with the longterm everyday monitored extreme stress data of bridge health monitoring (BHM) system. Then the improved Gaussian mixed particle filter (IGMPF) prediction algorithm was introduced which was obtained by using extended Kalman filter (EKF) and GMPF. IGMPF can predict onestep forward prediction distribution parameters of monitored extreme stress and the posteriori distribution parameters of extreme stress state variable. Finally, an actual example was provided to illustrate the application and feasibility of the IGMPF algorithm built. The IGMPF prediction algorithm can not only obtain the reasonable importance functions of monitored extreme stress states, but also solve the problems of shortterm prediction and low precision of the traditional prediction methods. It provides a theoretical foundation for dynamic response prediction of the actual BHM.

Key words:monitored extreme stress data; nonlinear dynamic model; extended Kalman filter; Gaussian mixed particle filter; improved Gaussian mixed particle filter