Identifying traffic-induced bridge excitations using an optimised state estimation method

Knowledge of the structural excitations induced in bridges due to traffic loading is an important component of structural health monitoring. However, direct measurement of such traffic-induced structural excitations in real-world structures is extremely challenging. While model-based methods provide indirect ways to estimate structural excitation, uncertainties associated with modelling have not been effectively considered in currently available methods for the identification of traffic-induced excitations. An optimised state estimation method that can accurately identify such traffic-induced structural excitations under uncertainties is proposed. The method uses an augmented Kalman filter (AKF) and a genetic algorithm (GA). However, the selection of error covariance values on the model, measurement and excitations is a critical challenge when using the AKF, especially for cases when excitations are spatially distributed over a large number of locations. The proposed method addresses such issues using GA-based optimisation with the objective of minimising the estimation error between measured and estimated excitations. Furthermore, heterogeneous structural measurements are used in the identification of excitations to improve the accuracy and stability of the process.