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Data-driven train set crash dynamics simulation.

Tang, Z., Zhu, Y., Nie, Y., Guo, S., Liu, F., Chang, J. and Zhang, J. J., 2016. Data-driven train set crash dynamics simulation. Vehicle System Dynamics, 55 (2), 149-167.

Full text available as:

VSD_Data-driven_train_set_crash_dynamics_simulation.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.


DOI: 10.1080/00423114.2016.1249377


© 2016 Informa UK Limited, trading as Taylor & Francis GroupTraditional finite element (FE) methods are arguably expensive in computation/simulation of the train crash. High computational cost limits their direct applications in investigating dynamic behaviours of an entire train set for crashworthiness design and structural optimisation. On the contrary, multi-body modelling is widely used because of its low computational cost with the trade-off in accuracy. In this study, a data-driven train crash modelling method is proposed to improve the performance of a multi-body dynamics simulation of train set crash without increasing the computational burden. This is achieved by the parallel random forest algorithm, which is a machine learning approach that extracts useful patterns of force–displacement curves and predicts a force–displacement relation in a given collision condition from a collection of offline FE simulation data on various collision conditions, namely different crash velocities in our analysis. Using the FE simulation results as a benchmark, we compared our method with traditional multi-body modelling methods and the result shows that our data-driven method improves the accuracy over traditional multi-body models in train crash simulation and runs at the same level of efficiency.

Item Type:Article
Additional Information:This is an Accepted Manuscript of an article published by Taylor & Francis in Vehicle System Dynamics on 06/10/2016, available online:
Uncontrolled Keywords:Train sets crash; data-driven modelling; dynamics simulation; crash dynamics; parallel random forest; machine learning
Group:Faculty of Media & Communication
ID Code:25184
Deposited By: Symplectic RT2
Deposited On:12 Dec 2016 12:26
Last Modified:14 Mar 2022 14:01


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