Citation

Tang Z, Zhu Y, Nie Y, Guo S, Liu F, Chang J, Zhang J. Veh. Syst. Dyn. 2017; 55(2): 149-167.

Copyright

(Copyright © 2017, Informa - Taylor and Francis Group)

DOI

10.1080/00423114.2016.1249377

PMID

unavailable

Abstract

Traditional 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.

Keywords

data-driven modelling; machine learning; crash dynamics; dynamics simulation; parallel random forest; Train sets crash