4.7 Article

Verification of stochastic seismic analysis method and seismic performance evaluation based on multi-indices for high CFRDs

Journal

ENGINEERING GEOLOGY
Volume 264, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.enggeo.2019.105412

Keywords

High CFRDs; Stochastic seismic analysis; GPDEM; MCM; Multi-indices; Seismic performance evaluation

Funding

  1. National Key R&D Program of China [2017YFC0404904]
  2. National Natural Science Foundation of China [51979026, 51779034, 51809034, 51679029]
  3. Postdoctoral Innovative Talent Support Program of China [BX20190057]
  4. Natural Science Foundation of Liaoning Province [20180551100]

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Many high concrete-faced rockfill dams (CFRDs) are located in areas with high earthquake intensity where the ground motions are characterized by randomness; consequently, it is significant to study the seismic responses and evaluate the seismic performance using dynamic time-history analysis by a stochastic vibration method based on failure probability theory. In this paper, a recently developed generalized probability density evolution method (GPDEM) coupled with a spectral representation-random function method is verified to be suitable for strongly nonlinear structures in high CFRDs during earthquakes by comparing the accuracy and efficiency of the GPDEM with those of the Monte Carlo method (MCM). The GPDEM combined with the currently deterministic dam finite element time-history response analysis using a series of simple and common methods, is adopted to analyze the stochastic seismic responses, dynamic probability evaluation and failure probability of high CFRDs subjected to stochastic earthquake excitation. The statistical and probabilistic information of the typical physical quantities are compared between the GPDEM and MCM after a series of deterministic dynamic calculations, and the dynamic nonlinear behavior of rockfills and the random characteristics of ground motions are presented. The strong correspondence between the results obtained using the traditional stochastic probability MCM analysis and the GPDEM analysis demonstrates the accuracy and effectiveness of the newly proposed method despite its significantly lower computational burden. Finally, the failure probabilities of a high CFRD with different failure grades based on three universal evaluation indices are determined by constructing a virtual GPDEM process. The results demonstrate that the GPDEM shows promise as an approach that can reliably analyze strongly nonlinear structures, such as earth-rockfill dams and other geotechnical engineering structures.

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