Abstract
The uncertainty in parameter estimation arises from structural systems’ input and output measured errors and from structural model errors. An experimental verification of the shuffled complex evolution metropolis algorithm (SCEM-UA) for identifying the optimal parameters of structural systems and estimating their uncertainty is presented. First, the estimation framework is theoretically developed. The SCEM-UA algorithm is employed to search through feasible parameters’ space and to infer the posterior distribution of the parameters automatically. The resulting posterior parameter distribution then provides the most likely estimation of parameter sets that produces the best model performance. The algorithm is subsequently validated through both numerical simulation and shaking table experiment for estimating the parameters of structural systems considering the uncertainty of available information. Finally, the proposed algorithm is extended to identify the uncertain physical parameters of a nonlinear structural system with a particle mass tuned damper (PTMD). The results demonstrate that the proposed algorithm can effectively estimate parameters with uncertainty for nonlinear structural systems, and it has a stronger anti-noise capability. Notably, the SCEM-UA method not only shows better global optimization capability compared with other heuristic optimization methods, but it also has the ability to simultaneously estimate the uncertainties associated with the posterior distributions of the structural parameters within a single optimization run.
Highlights
Nowadays, parameter estimation with high accuracy and practicality is essential in civil engineering when assessing the performance of structural systems; a contractor, engineer, or assessor will use it when evaluating such issues as reliability predictions, structural control, and structural health monitoring
Parameter identification has been successfully investigated based on numerical simulations Note and that compared with theThe linear structural system
Acceleration historiesrequires prediction solely corresponding to the estimate the posterior distribution of complex structural model parameters with very little posterior distribution of the parameter estimates displays a bias in a certain time period, indicating prior information
Summary
Parameter estimation with high accuracy and practicality is essential in civil engineering when assessing the performance of structural systems; a contractor, engineer, or assessor will use it when evaluating such issues as reliability predictions, structural control, and structural health monitoring. While some global optimization methods focus on finding the best set of parameters, a realistic assessment of parametric uncertainty in structural systems has not been given enough attention. Neither errors in the measured data nor uncertainties in model predictions are rigorously accounted for [16] These global optimization methods are extensively used and are robust and efficient for parameter estimation, finding out a unique “optimal” parameter set remains a huge challenge, which has a significantly better performance compared with other feasible parameter sets [17]. A parameter identification method is employed to assess uncertainty in the parameter estimates of structural models using the SCEM-UA algorithm.
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