In this work, a new finite element (FE) model calibration method of concrete dams based on strong-motion records and multivariate relevant vector machines (MRVM) is proposed. The modal features of a dam are extracted using second order blind identification (SOBI) based method at first. For some selected combinations of uncertain parameters of the FE model using the Latin hypercube design, the corresponding structural modal features are calculated using the finite element method (FEM). With these data, a procedure to calibrate the uncertain parameters of a dam’s dynamic FE model is developed. By taking the uncertain parameters as inputs and the calculated structural modal features using FEM as outputs, the MRVM model is trained to record the complex relationship between them. Then, the genetic algorithm (GA) is adopted to solve the optimization problem corresponding to the dynamic FE model calibration problem, and the trained MRVM model, instead of FEM, is used to obtain the modal parameters of a dam for different feasible solutions during the optimization search process to improve the computational efficiency. Using the simulated seismic response records of a numerical example the accuracy, robustness and computation efficiency of the proposed dynamic FE model calibration method is verified. The analysis result using the strong-motion records of a realistic concrete dam indicates that the proposed dynamic FE model calibration method has good performance.
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