Updating Bayesian detection of mechanical constants of thin-walled box girders based on Powell theory

Based on routine Bayesian theory, updating Bayesian error function of mechanical constants of thin-walled box girder was derived. Combined with automatic search scheme of polynomial interpolation series of optimal step length, the Powell optimization theory was utilized to implement the stochastic detection of mechanical constants of thin-walled box girder. Then, the Powell detection steps were presented in detail, and the Powell detection procedure of mechanical constants of thin-walled box girder was compiled, in which the mechanical analysis of thin-walled box girder was completed based on finite strip element method. Through some classic examples, it is obtained that Powell detection of mechanical constants has numerical stability and convergence, which proves that the present method and the compiled procedure are correct and reliable. During constants' iterative processes, the Powell theory is irrelevant with the calculation of partial differentiation in finite strip element method, which indicates high computation efficiency of the studied method. The stochastic performances of systematic constants and systematic responses are simultaneously included in updating Bayesian error function. The optimal step length is solved by search method of polynomial interpolation; without the need of predetermining the interval, the optimal step length locates.