A two-step framework for stochastic dynamic analysis of uncertain vehicle-bridge system subjected to random track irregularity. (September 2021)

• Record Type: Journal Article
• Authors: Yu, Helu; Wang, Bin; Li, Yongle; Gao, Zongyu
• Is Part Of: Computers & structures. Volume 253(2021)
• Publication Date: 2021-09
• Subjects: Vehicle-bridge interaction -- Stochastic dynamic analysis -- Improved explicit time-domain method -- Dimension-adaptive integration -- Random track irregularity -- Parametric uncertainty; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2021.106583
• Languages: English
• ISSNs: 0045-7949

Abstract

Highlights: Novel framework for stochastic dynamic analysis of vehicle-bridge system. Consideration of both uncertain parameters and random track irregularity. Explicit time-domain method improved to evaluate conditional response statistics. Dimension-adaptive Gaussian integration proposed to evaluate total response statistics. High accuracy and efficiency of proposed framework validated by numerical examples. Abstract: This paper presents a two-step probabilistic framework for stochastic dynamic analysis of the coupled vehicle-bridge system (VBS) involving both uncertain parameters and random track irregularity. Firstly, an explicit time-domain method is extended to evaluate the conditional response statistics of the VBS subjected to random irregularity at specific parameter points, and an efficient spectral representation-based formulation is proposed to calculate the excitation correlation matrix required in the analysis. Then, the total response statistics are further evaluated by Gaussian integration of the conditional ones based on the total probability theorem, and an efficient dimension-adaptive algorithm is proposed to collocate the required quadrature points. In the numerical examples, the proposed method is used to study a typical VBS model with both deterministic or random parameters; several other existing methods are employed to illustrate the advantages of the proposed method. The results indicate that the proposed framework is an accurate and efficient method for stochastic dynamic analysis of the vehicle-bridge interaction problem.