徐斌

个人信息Personal Information

教授

博士生导师

硕士生导师

任职 : 工程抗震研究所副所长

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:水利工程系

学科:水工结构工程. 防灾减灾工程及防护工程. 岩土工程

办公地点:辽宁省大连市高新园区大连理工大学四号实验楼101

联系方式:xubin@dlut.edu.cn

电子邮箱:xubin@dlut.edu.cn

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

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论文类型:期刊论文

第一作者:Xu, Bin

通讯作者:Pang, R (reprint author), Dalian Univ Technol, Fac Infrastruct Engn, Sch Hydraul Engn, Dalian 116024, Peoples R China.

合写作者:Pang, Rui,Zhou, Yang

发表时间:2020-01-01

发表刊物:ENGINEERING GEOLOGY

收录刊物:SCIE

卷号:264

ISSN号:0013-7952

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

摘要: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.