Bayesian Model Updating for System and Damage Identification of Bridges Using Synthetic and Field Test Data

The finite element (FE) models of bridges are vastly used for structural analysis. However, these initial models – developed from as-built drawings – cannot adequately present the real-world bridges due to the inherent modeling uncertainties, irregularities during construction, or aging. The uncertain model parameters can be estimated using Bayesian model updating techniques wherein the initial model is updated using measured responses. The updated finite element model can be used for structural health monitoring and damage diagnosis. This study presents a new framework for operational monitoring and damage diagnosis of bridges through the integration of finite element models with bridge vibration responses and vehicle tracking data using a Bayesian FE model updating method. First, the framework is verified in a simulation environment via synthetic data obtained from a finite element model of the San Roque Canyon Bridge located in Santa Barbara, California. Then, the proposed method is employed using real-world data collected from a pair of full-scale girders at the Turner-Fairbank Highway Research Center in McLean, Virginia. The performance of the employed approach is evaluated through a comparison of the estimated model and previously observed damage in the structure. Taken together, the results demonstrate the capabilities of the proposed framework to diagnose potential damages in real-world bridge structures.