Broadband dynamic load identification using augmented Kalman filter

Knowledge of the input forces to systems is crucial for system identification, structural control and structural health monitoring. However, in many engineering structures, direct measurement of the applied input forces, e.g. wind loading, earthquake loads, forces from traffic on a bridge, etc. is not feasible. In this study, an indirect model-based method is developed by means of state augmentation in Kalman filter to estimate the input loading from dynamic characteristics and measured responses of the structural systems. The effectiveness of the proposed method is numerically validated with a truss bridge model; the augmented Kalman filter used along with multimetric measurements of acceleration and strain shows accurate results in estimating both low- and high-frequency components of the input excitation.