Resonance Analysis of Train–Track–Bridge Interaction Systems with Correlated Uncertainties

Xin, Lifeng; Li, Xiaozhen; Zhang, Jiaxin; Zhu, Yan; Xiao, Lin

doi:10.1142/s021945542050008x

Cited by 25 publications

(7 citation statements)

References 47 publications

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“…In the aspects of uncertainty quantification, Mao et al (2019) evaluated the uncertainty upper and lower bounds of random time-history responses of the train-bridge coupled system based on the probability density evolution method. In the first time, Xin et al (2020a) defined the random resonance and random cancellation phenomena in the train-induced vibrations on bridge, characterized the probability distribution of the critical speeds, and investigated the influences of random resonance on the random responses of train-trackbridge coupled system. Xiao et al (2019b) explored the effects of track irregularities on the random responses of the train-bridge system.…”

Section: Vehicle-bridge Random Dynamicsmentioning

confidence: 99%

Review of annual progress of bridge engineering in 2019

Zhao

Yuan

Wei

et al. 2020

ABEN

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Bridge construction is one of the cores of traffic infrastructure construction. To better develop relevant bridge science, this paper introduces the main research progress in China and abroad in 2019 from 13 aspects, including concrete bridges and the high-performance materials, the latest research on steel-concrete composite girders, advances in box girder and cable-supported bridge analysis theories, advance in steel bridges, the theory of bridge evaluation and reinforcement, bridge model tests and new testing techniques, steel bridge fatigue, wind resistance of bridges, vehicle-bridge interactions, progress in seismic design of bridges, bridge hydrodynamics, bridge informatization and intelligent bridge and prefabricated concrete bridge structures.

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Section: Vehicle-bridge Random Dynamicsmentioning

confidence: 99%

Review of annual progress of bridge engineering in 2019

Zhao

Yuan

Wei

et al. 2020

ABEN

Self Cite

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“…Apart from the PDEM, Xu et al [22] combined the Monte-Carlo method (MCM) and Karhunen-Loève expansion (KLE) to model the random field of track-bridge systems. Xin et al [23] investigated train-induced resonance analysis by introducing a random propagation process into the VBI. e Nataf-transformation-based point estimation method is applied to generate pseudorandom variables following arbitrarily correlated probability distributions.…”

Section: Introductionmentioning

confidence: 99%

“…Despite a number of studies on the stochastic analysis, the main focus placed on train-bridge resonance phenomenon is still inadequate (except for Ref. [23]), and some important information is likely to be overlooked in this regard.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Analysis on the Resonance of Railway Trains Moving over a Series of Simply Supported Bridges

Zhang

Jin

Luo

et al. 2021

Shock and Vibration

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Resonance problems encountered in vehicle-bridge interaction (VBI) have attracted widespread concern over the past decades. Due to system random characteristics, the prediction of resonant speeds and responses will become more complicated. To this end, this study presents stochastic analysis on the resonance of railway trains moving over a series of simply supported bridges with consideration of the randomness of system parameters. A train-slab track-bridge (TSB) vertically coupled dynamics model is established following the basic principle of vehicle-track-coupled dynamics. The railway train is composed of multiple vehicles, and each of them is built by seven rigid parts assigned with a total of 10 degrees of freedom. The rail, track slab, and bridge are considered as Euler–Bernoulli beams, and the vibration equations of which are established by the modal superposition method (MSM). Except for the nonlinear wheel-rail interaction based on the Hertz contact theory, the other coupling relations between each subsystem are assumed to be linear elastic. The number theory method is employed to obtain the representative sample point sets of the random parameters, and the flow trajectories of probabilities for the TSB dynamics system are captured by a probability density evolution method (PDEM). Numerical results indicate that the maximum bridge and vehicle responses are mainly dominated by the primary train-induced resonant speed; the last vehicle of a train will be more seriously excited when the bridges are set in resonance by the train; the resonant speeds and responses are rather sensitive to the system randomness, and the possible maximum amplitudes predicted by the PDEM are significantly underestimated by the traditional deterministic method; optimized parameters of the TSB system are preliminary obtained based on the representative point sets and imposed screening conditions.

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“…Xu et al [9,10] analyzed the vehicle-track coupled system based on the multi-finite-element method. Xin et al [11] analyzed the resonance of bridge considering the randomness of parameters, and Xin et al [12] examined the uncertainty and sensitivity of parameters of the vehicle-bridge system. Meanwhile, Xiang et al [13] analyzed the vehicle-bridge coupled dynamic system after considering the creep effect.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Dynamic Response of the Bridge‐Vehicle System considering Random Road Roughness Based on the Moment Method

Feng

Huang

Wan

et al. 2021

Advances in Civil Engineering

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The bridge-vehicle interaction (BVI) system vibration is caused by the vehicles passing through the bridge. The road roughness has a great impact on the system vibration. In this regard, poor road roughness is known to affect the comfort of the vehicle crossing the bridge and aggravate the fatigue damage of the bridge. Road roughness is usually regarded as a random process in numerical calculation. To fully consider the influence of road roughness randomness on the response of the BVI system, a random BVI model was established. Thereafter, the random process of road roughness was expressed by Karhunen–Loeve expansion (KLE), after which the moment method was used to calculate the maximum probability value of the BVI system response. The proposed method has higher accuracy and efficiency than the Monte Carlo simulation (MCS) calculation method. Subsequently, the influences of vehicle speed, roughness grade, and bridge span on the impact factor (IMF) were analyzed. The results show that the road roughness grade has a greater impact on the bridge IMF than the bridge span and vehicle speed.

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Resonance Analysis of Train–Track–Bridge Interaction Systems with Correlated Uncertainties

Cited by 25 publications

References 47 publications

Review of annual progress of bridge engineering in 2019

Review of annual progress of bridge engineering in 2019

Stochastic Analysis on the Resonance of Railway Trains Moving over a Series of Simply Supported Bridges

Evaluating the Dynamic Response of the Bridge‐Vehicle System considering Random Road Roughness Based on the Moment Method

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