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A detector is constructed based on the above methodology, and a model updating strategy is also provided.
In this work, we propose a new nonlinear model updating strategy based on global/local nonlinear system identification of the dynamics.
A sensitivity-based finite element model updating strategy is used to detect, localize and quantify damage at each damage state considered.
The procedure combines the gap metric analysis to determine a control-relevant nominal model and the model updating strategy using low-order filters.
The verifications show that the proposed FE model updating strategy based on the data fusion results in more accurate assessment of both structural properties and boundary conditions than the traditional FE model updating using translational responses only.
Besides, a novel model updating strategy is developed to address the issues of 1) which sample points should be sent for simulation analysis to improve the optimization quality, and 2) whether the low-fidelity (LF) model or the high-fidelity (HF) model should be selected to run for a selected sample point.
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Therefore, application of nonlinear FE model updating strategies is recommended in future studies to resolve the errors caused by structural response nonlinearity.
Our objective in this work is to provide a better understanding of the various model updating strategies that utilize mathematical means to update a computer model based on both physical and computer observations.
To improve the accuracy and efficiency of computation model for complex structures, the stochastic model updating (SMU) strategy was proposed by combining the improved response surface model (IRSM) and the advanced Monte Carlo (MC) method based on experimental static test, prior information and uncertainties.
According to the prediction of the approximation models, a move limits updating strategy is employed to determine the design variable move limits for the next iteration.
The conventional updating strategy for an NIR model is based on new available samples.
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