Structural flexibility identification and fast-Bayesian-based uncertainty quantification of a cable-stayed bridge

Abstract

Most current uncertainty analyses are limited to the basic modal parameters such as frequencies and mode shapes, which are not enough to evaluate structural performance. In order to quantify the uncertainty of the deep parameters, a quantitative technique combining the mass-changing strategy and the fast Bayesian fast Fourier transform (FFT) approach is proposed. Basic modal parameters and their variance and coefficient of variation (c.o.v) are obtained from the fast Bayesian FFT approach, which only uses the ambient testing data. Deep parameters (scaling factor and flexibility matrix) are calculated on the mass-changing strategy. On this basis, the uncertainty quantification of deep parameters is strictly derived by the first-order expansion and used to predict the confidence interval of deflection. In this study, a static load testing of the Sutong Bridge is utilized to verify the effectiveness and reliability of the proposed uncertainty quantification technique. The structural flexibility matrix and its confidence interval are identified and then applied to predict the deflection of the main span under truck loads. The predicted results agree well with the displacement measurements, which are also within the estimated confidence interval.