Non-stationary random vibration analysis of a 3D train–bridge system using the probability density evolution method

Yu, Zhiwu; Mao, Jianfeng; Guo, Fengqi; Guo, Wei

doi:10.1016/j.jsv.2015.12.002

Cited by 92 publications

(20 citation statements)

References 34 publications

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“…Arvidsson, Andersson, and Karoumi (2019) employ a sample size of 24 rail irregularity realisations, also focusing on the response scatter and the wheel-rail contact interaction. An alternative stochastic approach to direct simulation is presented by Yu, Mao, Guo, and Guo (2016), where a probability density evolution method is applied to evaluate response statistics of a railway bridge with random rail imperfections. As a reference solution serve the outcomes of a Monte Carlo simulation with large samples of 5000 random profiles.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic View on the Effect of Random Rail Irregularities on Railway Bridge Vibrations

Salcher

Adam

Kuisle

2019

Structure and Infrastructure Engineering

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This contribution studies dynamic amplification factors that consider the impact of random rail irregularities on the deflection and acceleration response of simple railway bridges. Response statistics for a set of plane fully coupled bridge-vehicle interaction models with random irregularity profiles are derived by Monte Carlo simulations. The underlying samples of random irregularity profiles are generated from a power spectral density function, assuming that the uneven rail surface is governed by a Gaussian stochastic process in space. A modified bootstrap method is used to identify the required sample size of profiles for sufficiently accurate description of the random response. On the basis of this pilot study, a parametric response examination of short span railway bridges with variable length, fundamental frequency, structural mass and damping, and track quality in a range commonly observed in real structures is conducted. A statistical representation of the uncertain bridge response is employed for analytical modelling of the response amplification due to random rail irregularities considering limit state-based exceedance probabilities. The results are compared to outcomes of existing simplified approaches in design codes used to take into account the effect of rail irregularities on the dynamic bridge response, showing the improvement of the developed framework.

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Section: Introductionmentioning

confidence: 99%

A Stochastic View on the Effect of Random Rail Irregularities on Railway Bridge Vibrations

Salcher

Adam

Kuisle

2019

Structure and Infrastructure Engineering

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“…Fourthly, by applying the mechanical similarity mentioned in the specimen design section, it is speculated that the residual midspan deflection for the bridge on site is f r ≈ 2.0 × 6 = 12.0 mm, when the bridge is subjected to the train loading with a 30-ton axle weight experiencing cycle number N ≈ 3.0 × 10 6 . Therefore, the performance deterioration of the bridge may cause serious traffic safety problems, especially considering the dynamic coupling of the train and the bridge [28,29].…”

Section: Performance Deterioration Of Heavy-haul Railwaymentioning

confidence: 99%

Performance Deterioration of Heavy-Haul Railway Bridges under Fatigue Loading Monitored by a Multisensor System

Shan

Yuan

et al. 2018

Journal of Sensors

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Heavy-haul railway bridges play an increasingly essential role in the transportation in China due to the increasing transport volume. The performance deterioration of the scale models of a typical heavy-haul railway bridge under fatigue loading is monitored in this work, based on a multisensor system including the fiber-reinforced polymer optical fiber Bragg grating and electrical resistance strain gauges, linear variable displacement transducer, and accelerometer. Specifically, by monitoring/ observing on the failure mode, fatigue life, load-midspan deflection response, material strain development, and so forth, this work develops an S-N model by comparing the relationship between fatigue life and rebar stress range with that between fatigue life and load level and proposes a damage evolution model considering the coupling of the stiffness degradation and inelastic deformation of specimens. It is found that the fatigue life of specimens is determined by the fatigue life of the rebar at the bottom and it may be lower than 2.0 million cycles with a 30-ton axle weight when environmental factors are taken into account. The predictions of the models agree well with experimental results. Therefore, this work furthers the understanding of the fatigue performance deterioration of the bridges by using a multisensor system.

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“…Zhang et al also have used the PEM and the precise integration method (PIM) to compute the nonstationary random responses of three-dimensional train-bridge systems subjected to lateral horizontal earthquakes [14]. Yu et al [15] studied the random vibrations of a linearized vehicle-railbridge coupling system by generalized probability density evolution theory (originally formulated by Li and Chen [16]), which is more efficient than the MCM. Xu et al [17,18] proposed a stochastic space-time analysis method for vehicle-track-coupled power systems.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Analysis of Nonlinear Vehicle‐Track Coupled Dynamic System and Its Application in Vehicle Operation Safety Evaluation

Liu

Zeng

Wei-dong

et al. 2019

Shock and Vibration

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This paper presents a vehicle operation safety evaluation model; to this end, a nonlinear vehicle-track coupled dynamic system stochastic analysis model under random irregularity excitations based on probability density evolution method was developed. The nonlinear coupled vehicle-track dynamic system is used to accurately describe the wheel-rail contact state. The stochastic function-spectral representation is used to simulate the random track irregularity in the time domain for the first time; consequently, the frequency components in the irregularity are preserved and random variables are reduced. In the process of evaluating the safety of train operation, the probability evolution, reliability of evaluation indices for different limit values, and evaluation indices for different probability limits are calculated for more accurate evaluation. The dynamic model and safety evaluation method was verified using the Zhai-model and Monte Carlo method. The results show that, when the probability guarantee is increased, the running safety index of the vehicle increases more rapidly with running speed and the left/right wheel-rail derailment coefficient increases rapidly at running speeds above 400 km/h. The computational model provides a novel direction for vehicle operation safety evaluation.

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Non-stationary random vibration analysis of a 3D train–bridge system using the probability density evolution method

Cited by 92 publications

References 34 publications

A Stochastic View on the Effect of Random Rail Irregularities on Railway Bridge Vibrations

A Stochastic View on the Effect of Random Rail Irregularities on Railway Bridge Vibrations

Performance Deterioration of Heavy-Haul Railway Bridges under Fatigue Loading Monitored by a Multisensor System

Stochastic Analysis of Nonlinear Vehicle‐Track Coupled Dynamic System and Its Application in Vehicle Operation Safety Evaluation

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