Dynamic Response of Railway Vehicles Running on Long-Span Cable-Stayed Bridge Under Uniform Seismic Excitations

Li, Yongle; Zhu, Sheng; Cai, C.S.; Yang, Cheng; Shi-zhong, Qiang

doi:10.1142/s0219455415500054

Cited by 28 publications

(14 citation statements)

References 21 publications

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“…Modeling of vehicle-bridge system: (a) mass-spring-damper model of vehicle (Li et al, 2005, 2016) and (b) bridge cross-section.…”

Section: Response Predictions Of Vertical Train-bridge Systemsmentioning

confidence: 99%

“…The vehicle dynamic system can be modeled as a massspring-damper system (Li et al, 2005(Li et al, , 2016, as shown in Figure 8(a). The train model has two suspension systems consisting primarily of seven rigid bodies: one vehicle body, two bogies, and four wheelsets.…”

Section: Models Of Vehicle and Bridgementioning

confidence: 99%

“…Due to the fact that only the vertical track irregularity is considered, the track irregularities Figure 8. Modeling of vehicle-bridge system: (a) mass-spring-damper model of vehicle (Li et al, 2005(Li et al, , 2016 and (b) bridge crosssection.…”

Section: Models Of Vehicle and Bridgementioning

confidence: 99%

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Predictions of vertical train-bridge response using artificial neural network-based surrogate model

Han

Xiang

et al. 2019

Advances in Structural Engineering

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To improve the efficiency of reliability calculations for vehicle-bridge systems, we present a surrogate modeling method based on a nonlinear autoregressive with exogenous input artificial neural network model and an important sample, which can forecast responses of dynamic systems, such as vehicle-bridge systems, subjected to stochastic excitations. We also propose a process to analyze the method. A quarter-vehicle model is used to verify the proposed method’s precision, and the nonlinear autoregressive with exogenous input artificial neural network model is used to predict responses of vertical vehicle-bridge systems. The results show that, compared to other training samples, the nonlinear autoregressive with exogenous input artificial neural network model has better prediction accuracy when the sample with the maximum response is considered as an important sample and is used to train the nonlinear autoregressive with exogenous input artificial neural network model, and it requires only two-time numerical simulation (or Monte Carlo simulation) at most, which is used in the training of the nonlinear autoregressive with exogenous input artificial neural network model.

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“…Modeling of vehicle-bridge system: (a) mass-spring-damper model of vehicle (Li et al, 2005, 2016) and (b) bridge cross-section.…”

Section: Response Predictions Of Vertical Train-bridge Systemsmentioning

confidence: 99%

Section: Models Of Vehicle and Bridgementioning

confidence: 99%

See 1 more Smart Citation

Predictions of vertical train-bridge response using artificial neural network-based surrogate model

Han

Xiang

et al. 2019

Advances in Structural Engineering

Self Cite

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“…With the rapid worldwide development of high-speed railways, the use of long-span railway bridges in high-intensity seismic regions or strong wind regions is becoming increasingly common (Chen et al, 2019; Deng et al, 2020; Li et al, 2015b; Liang et al, 2019; Olmos and Astiz, 2018; Wang et al, 2014; Zhou and Chen, 2015). Such strong excitations can cause nonlinear bridge behaviors, including geometric nonlinearity (Apaydin et al, 2016), material nonlinearity (Yang et al, 2018), and boundary nonlinearity at the bearings (Borjigin et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A real-time co-simulation solution for train–track–bridge interaction

Gong

Zhu

Wang

et al. 2020

Journal of Vibration and Control

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This study develops a real-time co-simulation solution that combines ANSYS and MATLAB for investigating of the train–track–bridge dynamic interactions, with the aim of providing an effective and robust method for analyzing and assessing the dynamic responses of running train and bridge, considering their complex nonlinear behaviors under extreme excitations such as earthquake and strong wind. The train–track–bridge coupled system consists of the train subsystem, the track subsystem, and the bridge subsystem. With the adoption of a previously developed efficient multi-time-step method, the train subsystem and the track subsystem are analyzed in MATLAB with a small time-step, whereas the bridge subsystem is analyzed in ANSYS using a large time-step. At each large time-step, the train–track subsystem in MATLAB and the bridge subsystem in ANSYS are coupled by communication of the track reaction forces calculated in MATLAB and the dynamic responses at the track–bridge connecting points calculated in ANSYS. The co-simulation procedure is validated through a comparison of the numerical results from computer coding developed by the authors and the measurement data of a cable-stayed bridge. To demonstrate the effectiveness and robustness of the proposed solution, the responses of a train passing through a cable-stayed bridge under earthquake excitation are analyzed, with consideration of the geometrical nonlinearity of the bridge, using the co-simulation solution.

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“…This model has been widely used among researchers due to its simplicity. [1][2][3][4][5][6][7] However, the accuracy of this model was recently challenged by some researchers. 8,9 Based on the results from a¯eld study, Yin et al 8 found that the single-point model may overestimate the dynamic ampli¯cation of the bridge responses, especially under distressed bridge deck conditions.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Point Tire Model for Studying Bridge–Vehicle Coupled Vibration

Deng

Cao

Wang

et al. 2016

Int. J. Str. Stab. Dyn.

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The contact between a vehicle tire and the road surface has been usually assumed as a singlepoint contact in the numerical simulation of vehicle-bridge interacted vibrations. In reality, the tire contacts the road surface through a patch instead of a single point. According to some recent studies, the single-point tire model may overestimate the dynamic ampli¯cation of bridge responses due to vehicle loadings. A new tire model, namely, the multi-point tire model, is therefore proposed in this paper with the purpose of improving the accuracy of numerical simulation results over the single-point model, while maintaining a certain level of simplicity for applications. A series of numerical simulations are carried out to compare the e®ect of the proposed tire model with those of the existing single-point model and disk model on the bridge dynamic responses. The proposed tire model is also veri¯ed against the¯eld test results. The results show that the proposed multi-point tire model can predict the bridge dynamic responses with better accuracy than the single-point model, especially under distressed bridge deck conditions, and is computationally more e±cient and simpler for application than the disk model.

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Dynamic Response of Railway Vehicles Running on Long-Span Cable-Stayed Bridge Under Uniform Seismic Excitations

Cited by 28 publications

References 21 publications

Predictions of vertical train-bridge response using artificial neural network-based surrogate model

Predictions of vertical train-bridge response using artificial neural network-based surrogate model

A real-time co-simulation solution for train–track–bridge interaction

A Multi-Point Tire Model for Studying Bridge–Vehicle Coupled Vibration

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