Abstract Summary
This paper is dedicated to identifying the damage of simple structures by the Hamiltonian Monte Carlo(HMC) method. The data studied in this paper comes from the response of numerically simulated simple structures with a randomly distributed damage area. The uncertainties innated in the randomly generated damage distribution and structural material distribution are quantified by a set of random variables which is derived from the well-known spectral decomposition method Karhunen-Loéve expansion method. The fundamental assumption for this approach is that the structural damage is manifested in the changes in the low to mid-frequency vibration response of the structure. Hence point-to-point frequency response functions at a distributed set of locations are adopted to identify structural degradation parameters with Bayesian inference. Thus, a mature Bayesian algorithm is applied to parameterize the whole inverse identification process, and then the HMC method is utilized to identify the random variables in a high dimensional space. Some good results have been observed after applying this identification algorithm on a simply supported beam and a two-sided pinned plate, which proves the validity and accuracy of the derived algorithm.
