scholarly journals Nonlinear Stochastic Analysis of Footbridge Lateral Vibration Based on Probability Density Evolution Method

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Zheng Yang ◽  
Buyu Jia ◽  
Quansheng Yan ◽  
Xiaolin Yu ◽  
Yinghao Zhao

Footbridge lateral vibration remains an unsolved problem and is characterized by the following: (1) pedestrians are sensitive to bridge vibration, which causes the pedestrian’s excitation being dependent on the bridge vibration; (2) pedestrian lateral excitation is a stochastic process rather than a perfect periodic load. Therefore, footbridge lateral vibration is essentially a complex nonlinear stochastic vibration system. Thus far, an effective method of dealing with such nonlinear stochastic vibration of footbridges remains lacking. A framework based on the probability density evolution (PDE) method is presented. For the mathematical model, the parameter resonance model is used to describe the pedestrian-bridge interaction while treating the pedestrian lateral excitation as a narrow-band process. For the analysis method, PDE is used to solve the nonlinear stochastic equations in combination with the number theoretical and finite difference methods. The proposed method establishes a new approach in studying footbridge lateral vibration. First, PDE based on the small sample strategy avoids the large amount of computation. Second, the randomness of both structural parameters and pedestrian lateral excitation could be taken into consideration by the proposed method. Third, based on the probability results with rich information, the serviceability, dynamic reliability, and random stability analyses are realized in a convenient manner.

Aviation ◽  
2018 ◽  
Vol 22 (2) ◽  
pp. 45-54 ◽  
Author(s):  
ajad Saraygord Afshari ◽  
Seid H. Pourtakdoust

Reliability evaluation is a key factor in serviceability and safety analysis of air vehicles. Structural health monitoring methods have grown to a degree of maturity in many industries. However, there is a challenging interest to tie in SHM with reliability assessment. In this respect, consideration of stochastic structural dynamics with SHM data and random loadings opens a new chapter in failure prevention. The current study focuses on the stochastic behavior of structures as a way to relate SHM data with reliability. In this respect, uncertain factors such as atmospheric turbulence, structural parameters, and sensor outputs are considered in the process of reliability assessment. Firstly, an experimental evaluation is conducted using a simple cantilevered beam. Subsequently, system identification is weaved in with a probability density evolution equation for calculating the reliability of a wing structural component. Numerical simulations demonstrate that structural reliability of a typical WSC can be effectively evaluated. The proposed scheme paves the way for new SHM research topics such as online life prediction and reliability based failure prevention.


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