Abstract
Dynamic parameters of rockfill materials are an important component of seismic safety analysis of earth and rock dams, which are a complex of non-linear, time-varying, and multi-parameter uncertainties. Existing methods of parameter acquisition, such as indoor tests, are susceptible to environmental and dimensional influences, resulting in the acquired parameters not reflecting the true physical properties of the dam. To this end, this paper combines an adaptive cloud transformation algorithm with an RBF neural network to construct an adaptive cloud neural network model that considers the randomness and fuzziness among dam systems by quantifying the uncertainty factors and uses an actual engineering example from the Wenchuan earthquake to carry out an inverse analysis of the material dynamics parameters. The results show that the acceleration time histories, response spectra, and Fourier amplitudes of the dam measurement points agree well with the measured values, confirming the rationality of the inversion model; the method improves the accuracy and efficiency of dynamic parameter inversion, providing a reference for assessing and analyzing the properties of earth and rock dams under seismic loading.
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Funding
The authors received support provided by the National Natural Science Foundation of China (No. 51609087, No. 51709114), Key Scientific Research Projects of Colleges and Universities in Henan Province in 2021 (No. 21A570001), and Collaborative Innovation Center of Water Resources Efficient Utilization and Protection Engineering of Henan Province.
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Zhang, H., Song, Z., Sun, Y. et al. Back-analysis of dynamic property of earth-rock dam considering parameter uncertainly. Arab J Geosci 15, 1712 (2022). https://doi.org/10.1007/s12517-022-10983-w
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DOI: https://doi.org/10.1007/s12517-022-10983-w