Updated Bayes identification of displacement constants of curve indeterminate box girder with variable scale theory

Abstract

For curve indeterminate box girder, updated Bayes identification model of displacement constants was derived and studied with the variable-scale optimization theory. First, the updated Bayes objective function of displacement constants of the structure was founded. The gradient matrix of the objective function to displacement constants and the calculative covariance matrix were both deduced. Then, with finite curve strip element method, mechanical analysis of curve indeterminate box girder was completed. With automatic search scheme of quadratic parabola interpolation for optimal step length, the variable scale theory was utilized to optimize the updated Bayes objective function. Then, the identification steps were expounded, and the identification procedure was developed. Through typical examples, it is achieved that the updated Bayes identification model of displacement constants has numerical stability and perfect convergence. The stochastic performances of systematic parameters and systematic responses are simultaneously deliberated in updated Bayes objective function, which can synchronously take the actual measured information at different times into account. The variable-scale optimization method continually changes the spatial matrix scale to generate renewed search directions during the iterations, which certainly accelerates the identification of the displacement constants.