Probability density evolution analysis of stochastic nonlinear structure under non-stationary ground motions. Issue 8 (3rd August 2019)

• Record Type: Journal Article
• Authors: Liu, Zhangjun; Ruan, Xinxin; Liu, Zixin; Lu, Hailin
• Is Part Of: Structure and infrastructure engineering. Volume 15:Issue 8(2019)
• Page Start: 1049
• Page End: 1059
• Publication Date: 2019-08-03
• Subjects: Non-stationary stochastic ground motions -- nonlinear structures -- random parameters -- probability density evolution method -- dynamic reliability; Structural analysis (Engineering) -- Periodicals; Structural engineering -- Periodicals; Buildings -- Performance -- Periodicals; 620.005
• DOI: 10.1080/15732479.2019.1599963
• Languages: English
• ISSNs: 1573-2479

Abstract

Abstract: In the present paper, a framework of dimension-reduction modeling method is developed for a dual stochastic dynamic structural system of spectrum-compatible non-stationary stochastic ground motion processes and stochastic structures. With the aid of the proposed method, the random variables used to describe the stochastic characteristics of the non-stationary ground motion processes and the structural parameters are respectively represented by the one-elementary-random-variable functions, contributing to an entire stochastic dynamic structural system readily described by merely two elementary random variables. Owing to the fact that the number of the elementary random variables needed is extremely small, the set of the representative points associated with the elementary random variables can thus be selected by the widely-used number theoretical method, and then the probability density evolution method can be conveniently combined to conduct the dynamic response analysis and dynamic reliability evaluation of nonlinear stochastic structures. In the numerical examples, the probability density evolution analysis of an eight-storey reinforced concrete frame structure with random parameters subjected to spectrum-compatible non-stationary stochastic ground motion processes is investigated as a case study. Numerical results fully demonstrated the effectiveness and robustness of the proposed method.