Modified Hamiltonian Monte Carlo-based Bayesian finite element model updating of steel truss bridge

The aim of this study is to develop a Hamiltonian Monte Carlo-based algorithm for finite element model updating in the Bayesian framework. The proposed algorithm uses adaptive prior-based approach, which helps to generate the intermediate pdfs. Numerical analysis is carried out with different coefficient of variations of the prior for model updating. Guidelines for their proper selection procedure are also prescribed in this work. The efficiency of the proposed method is demonstrated using synthetic experiments and actual test data for updating the finite element model of a steel truss bridge. Finally, performance of this algorithm is compared with the standard Markov chain Monte Carlo algorithm to demonstrate its advantages.