Time-dependent pounding probability analysis between adjacent decks of bridges under non-stationary stochastic seismic excitations. (December 2020)

• Record Type: Journal Article
• Authors: Jia, Hong-Yu; Yue, Wei-Qin; Zheng, Shi-Xiong; Gou, Hong-Ye; Zhao, Can-Hui; You, Gang
• Is Part Of: Structures. Volume 28(2021)
• Page Start: 2355
• Page End: 2366
• Publication Date: 2020-12
• Subjects: Time-dependent probability -- Required separation gap width -- Non-stationary stochastic seismic excitation -- Pseudo excitation method -- Spatially varying ground motion; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2020.10.028
• Languages: English
• ISSNs: 2352-0124

Abstract

Abstract: This paper proposes a time-dependent probability method to predict the time-variant pounding risk, owing to this reason that the required separation gap width fails to accommodate the relative movement between two adjacent decks, of bridge systems subjected to non-stationary stochastic seismic excitations. Firstly the pseudo excitation method in random theory is utilized to transform the motion equation of system under the non-stationary stochastic seismic excitations into the transient response equation under deterministic external loads. And then the high precision direct integration method (HPDIM) is also employed to reduce the large number of transient analysis into two times for overcoming the challenges of strenuous computing effort. The time-dependent pounding system probability of bridges is derived by the combination of extreme value distribution theory and matrix-based system probability theory. Finally based on the proposed time-dependent probability method the numerical analysis of a real two span simply-supported beam bridge subjected to spatial and non-stationary ground motions is conducted herein for the pounding risk assessment. The results of this study provides in-depth insight into the time-dependent pounding probability of bridge systems subjected to spatially varying and non-stationary ground motions.