EFFICIENT MODEL UPDATING APPROACHES INTEGRATING ENSEMBLE LEARNING METHODS FOR DIFFERENT STRUCTURAL COMPLEXITY

Model uncertainty inevitably affects the accuracy and reliability of numerical model-based analysis. It is necessary to obtain an appropriate updating method which could determine the reasonable values of model parameter from measurements. A Bayesian model updating method is proposed combined with the transitional Markov chain Monte Carlo (TMCMC). Kriging predictor and polynomial chaos expansion (PCE) are utilized to construct surrogate models to reduce the computational burden. The proposed model updating methods are applied to two structural examples with different complexity, which stand for the high-dimensional linear model and the high-dimensional nonlinear model, respectively. The validity and accuracy of two surrogate models are investigated in the numerical examples. The advantages and limitations of the surrogate models-based updating approaches are also discussesed for different structural complexity. For the deficiency of surrogate models, it proposes an ensemble learning method to improve the model updating.