Empirical Investigation of the Effects of the Measurement-Data Size on the Bayesian Structural Model Updating of a High-Speed Railway Bridge

Bayesian structural model updating (SMU) is among the most powerful methods for estimating the bending stiffness and modal damping of high-speed railway (HSR) bridges and predicting their bridge-response-based resonance responses. Although studies indicated that the convergence to the true value as the observed data increase favored Bayesian inference, the data-size effects on the estimation accuracy have not been sufficiently investigated. Here, the maximum bridge deck acceleration upon the passage of a train, which is used in European bridge designs, is explored, and the data-size effect on the Bayesian SMU is empirically investigated. For an HSR bridge spanning approximately 50 m, the parameters and maximum acceleration of a beam model on which the moving loads act are updated by the Markov chain Monte Carlo simulation method using the measured maximum acceleration. A comparison of the estimated values with different measurement data revealed that the estimated values converged for three samples, when the data included the resonance state of the test bridge. Overall, the results can be employed to establish a logical method for determining the necessary field measurement specifications for ensuring the accuracy of Bayesian SMU.