Efficient model updating with incomplete modal data for structural health monitoring

An efficient Bayesian probabilistic method in conjunction with an auxiliary deterministic approach for structural model updating with incomplete modal data is presented. The proposed method consists of two steps: preliminary model updating using a deterministic approach followed by a Bayesian probabilistic model updating. In the beginning, an initial finite element (FE) model based on the design drawings and/or initial guesses is constructed for model updating. In the preliminary updating stage, an iterative technique based on eigenvalue equation and mode shape expansion is applied to obtain an initial estimate of the stiffness parameters. Finally, a probabilistic approach based on Bayesian framework is used to find the most probable values of the stiffness parameters as well as to quantify their associated uncertainties. The initial estimates obtained in the preliminary updating stage can facilitate the optimization problem in the Bayesian updating stage, which makes the algorithm more efficient and robust. The proposed method is demonstrated and verified by a shear building model using synthetic data. © 2013 Taylor & Francis Group, London