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

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