An artificial neural network model as a preliminary track design tool

Montalbán-Domingo, Laura; Fernández-Villa, Juan Antonio Villaronte; Sendra, Chele Esteve; Herráiz, Julia Irene Real

doi:10.1177/0954409715576366

Cited by 2 publications

(2 citation statements)

References 24 publications

(49 reference statements)

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“…Based on the force-displacement data of finite element simulation at different initial collision velocity, Tang et al 29,30 established a data-driven model and simulation framework for rail vehicles by introducing the surrogate model into the multi-rigid simulation model. Domingo et al 31 presents a predesign tool that is based on an artificial neural network (ANN). Lapedes et al 32…”

Section: Introductionmentioning

confidence: 99%

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Application of the collision mathematical model based on a BP neural network in railway vehicles

Zhu

Xiao

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part F:

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In order to enhance the learning performance of small-data-set models and improve the computation efficiency of finite element simulations of vehicle collision, the collision mathematical model (VCMM) based on the back-propagation (BP) neural network is established to predict the collision response data of a single car and marshalling cars at unknown velocities. The predicted results of VCMM were compared with the simulation results of the finite element method (FEM) to verify the model. The compared results show that the maximum relative errors of deformation, energy absorption and average interfacial force of a single vehicle are all below 8.5%, and the relative errors of the maximum compression of the C0 coupler and the internal energy of the A1 car among the marshalling cars are all less than 5%. In addition, the calculation time of the single car and marshalling cars collisions based on the VCMM are reduced by 24.36 and 61.8 times, respectively, compared with the FEM results, and the simulation calculation efficiency is greatly improved. The prediction result of VCMM will partially replace experimental and simulation results for crashworthiness and safety design of the vehicle structure in future studies.

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Section: Introductionmentioning

confidence: 99%

“…Domingo et al. 31 presents a pre-design tool that is based on an artificial neural network (ANN). Lapedes et al.…”

Section: Introductionmentioning

confidence: 99%

Application of the collision mathematical model based on a BP neural network in railway vehicles

Zhu

Xiao

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part F:

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Effect of vehicle speed on the dynamics of track transitions

Palomo

Barceló

Ribes-Llario

et al. 2017

Journal of Vibration and Control

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When a railway track presents an abrupt discontinuity of vertical stiffness (e.g., due to the presence of a structure), an amplification of the dynamic forces acting on both the vehicle and the track may be expected. Therefore, diverse solutions—generally known as track transitions—have been proposed in the literature to provide a smoother stiffness change between different track sections. An extensive research has been performed in recent years in order to better understand the dynamics of track transitions as well as the processes involved. However, to the best of the authors’ knowledge, no research has fully studied the effect of vehicle speed and only one value is generally used instead of considering a wider range. Hence, this paper explicitly analyzes the effect of train speed on the vibrational performance of four transition typologies: a concrete wedge; a hot mixed asphalt wedge; a geogrid; and a soil reinforcement. The results show that mitigation capacity of all the alternatives increases with train speed, the concrete wedge being the most effective solution. Moreover, the effects on vertical vibrations of the abrupt stiffness change are found to be significant only for high train speeds (220–300 km/h). Finally, an amplification effect due to resonance has been found for the concrete transition when a vehicle speed of 100 km/h is considered.

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An artificial neural network model as a preliminary track design tool

Cited by 2 publications

References 24 publications

Application of the collision mathematical model based on a BP neural network in railway vehicles

Application of the collision mathematical model based on a BP neural network in railway vehicles

Effect of vehicle speed on the dynamics of track transitions

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