The reasonable selection of dynamic parameters of earth-rock dams is very important for the seismic safety assessment of dams. Because of the size effects, the parameters acquired from the indoor test may additionally no longer exactly reflect the real dynamic characteristics of the dam. Therefore, it is essential to correct and update the dynamic parameters in accordance to the actual seismic dynamic characteristics of the dam. In this paper, by combining the Covariance-driven stochastic subspace identification (SSI-COV) method and the multiple population genetic algorithm (MPGA), an optimization and updating method for the dynamic parameters of earth-rock dams based on modal parameter identification is presented. The collected weak earthquake records of the dam allowed the modal parameters (the natural frequencies and mode shapes) identification based on SSI-COV method. The optimization updated is performed using the MPGA, which can obtain the optimal values of dynamic parameters of the dam and has good robustness within the optimization range of the numerical model. A typical numerical example is given to exhibit the feasibility and effectiveness of the presented method. Then the dynamic parameters of Liyutan Dam are updated by the proposed method according to the weak earthquake records, and the accuracy and applicability of the updated parameters were further verified by the strong earthquake records. The results show that the method proposed in this paper can provide a good idea for the calibration of dynamic parameters of earth-rock dams and has well practical engineering value.


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    Titel :

    Dynamic Parameters Updating of Earth-rock Dams Based on Modal Parameter Identification


    Weitere Titelangaben:

    KSCE J Civ Eng


    Beteiligte:
    Wang, Maohua (Autor:in) / Chi, Shichun (Autor:in) / Liu, Zhenping (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2023-10-01


    Format / Umfang :

    13 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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