Data-driven train set crash dynamics simulation

References

• Chadwick SG, Zhou N, Saat MR. Highway-rail grade crossing safety challenges for shared operations of high-speed passenger and heavy freight rail in the US. Saf Sci. 2014;68:128–137. doi: 10.1016/j.ssci.2014.03.003
   Web of Science ®Google Scholar
• Kimura S, Mochida T, Kawasaki T, et al. Evaluation of energy absorption of crashworthy structure for railway's rolling stock (numerical simulation applying damage-mechanics model). J Solid Mech Mater Eng. 2013;7:102–117. doi: 10.1299/jmmp.7.102
   Google Scholar
• Xie S, Liang X, Zhou H, et al. Crashworthiness optimisation of the front-end structure of the lead car of a high-speed train. Struct Multidiscip Optim. 2016;53:339–347. doi: 10.1007/s00158-015-1332-y
   Web of Science ®Google Scholar
• Shao H, Xu P, Yao S, et al. Improved multi-body dynamics for investigating energy dissipation in train collisions based on scaling laws. Shock Vib. 2016;2016:1–11. doi: 10.1155/2016/3084052
   Web of Science ®Google Scholar
• Afazov S, Denmark W, Yaghi A. Modelling aspects of the design of railway vehicle structures and their crashworthiness. J Rail Rapid Transit. 2015;229:1–15.
   Google Scholar
• Lu G. Energy absorption requirement for crashworthy vehicles. J Rail Rapid Transit. 2002;216:31–39. doi: 10.1243/0954409021531665
   Web of Science ®Google Scholar
• Milho JF, Ambrósio JAC, Pereira MFOS. Design of train crash experimental tests by optimization procedures. Int J Crashworthiness. 2004;9:483–493. doi: 10.1533/ijcr.2004.0304
   Web of Science ®Google Scholar
• Dias JP, Pereira MS. Optimization methods for crashworthiness design using multibody models. Comput Struct. 2004;82:1371–1380. doi: 10.1016/j.compstruc.2004.03.032
   Web of Science ®Google Scholar
• Milho JF, Ambrósio JAC, Pereira MFOS. A multibody methodology for the design of anti-climber devices for train crashworthiness simulation. Int J Crashworthiness. 2002;7:7–20. doi: 10.1533/cras.2002.0202
   Web of Science ®Google Scholar
• Han H-S, Koo J-S. Simulation of train crashes in three dimensions. Veh Syst Dyn. 2003;40:435–450. doi: 10.1076/vesd.40.6.435.17906
   Web of Science ®Google Scholar
• Sun YQ, Cole C, Dhanasekar M, et al. Modelling and analysis of the crush zone of a typical Australian passenger train. Veh Syst Dyn. 2012;50:1137–1155. doi: 10.1080/00423114.2012.656658
   Web of Science ®Google Scholar
• Zhou H, Zhang J, Hecht M. Three-dimensional derailment analysis of crashed freight trains. Veh Syst Dyn. 2014;52:341–361. doi: 10.1080/00423114.2014.881512
   Web of Science ®Google Scholar
• Pawlus W, Karimi HR, Robbersmyr KG. Data-based modeling of vehicle collisions by nonlinear autoregressive model and feedforward neural network. Inf Sci. 2013;235:65–79. doi: 10.1016/j.ins.2012.03.013
   Web of Science ®Google Scholar
• Fender J, Duddeck F, Zimmermann M. On the calibration of simplified vehicle crash models. Struct Multidiscip Optim. 2014;49:455–469. doi: 10.1007/s00158-013-0977-7
   Web of Science ®Google Scholar
• Taheri M, Ahmadian M. Machine learning from computer simulations with applications in rail vehicle dynamics. Veh Syst Dyn. 2016;54:653–666. doi: 10.1080/00423114.2016.1150497
   Web of Science ®Google Scholar
• Zhou H, Wang W, Hecht M. Three-dimensional override analysis of crashed railway multiple units. Veh Syst Dyn. 2012;50:663–674. doi: 10.1080/00423114.2011.631552
   Web of Science ®Google Scholar
• Baykasoglu C, Sunbuloglu E, Bozdag SE, et al. Crash and structural analyses of an aluminium railroad passenger car. Int J Crashworthiness. 2012;17:519–528. doi: 10.1080/13588265.2012.690591
   Web of Science ®Google Scholar
• Tang Z, Liu FJ, Guo SH, et al. Evaluation of coupled finite element/meshfree method for a robust full-scale crashworthiness simulation of railway vehicles. Adv Mech Eng. 2016;8.
   Web of Science ®Google Scholar
• Li S, Zheng Z, Yu J. Energy-absorbing structure design and crashworthiness analysis of high-speed trains. Explos Shock Waves. 2015;35:164–170.
   Google Scholar
• Fernández-Delgado M, Cernadas E, Barro S, et al. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res. 2014;15:3133–3181.
   Web of Science ®Google Scholar
• Kotsiantis S. Supervised machine learning: a review of classification techniques. Informatica. 2007;31:249–268.
   Google Scholar
• Criminisi A, Shotton J. Decision forests for computer vision and medical image analysis. Springer; 2013.
   Google Scholar
• Louppe G. Understanding random forests [Ph.D.]: University of Liège; 2014.
   Google Scholar
• Pereira M, Hecht M, Segurado V. LRV static-passive safety in urban railway systems; 2000 (SAFETRAM report).
   Google Scholar
• Klausen A, Tørdal SS, Karimi HR, et al. Firefly optimization and mathematical modeling of a vehicle crash test based on single-mass. J Appl Math. 2014;2014:1–10. doi: 10.1155/2014/150319
   Web of Science ®Google Scholar
• Standard E. EN15227: 2008. Railway applications—crashworthiness requirements for railway vehicle bodies, English Version, European Committee for Standardization; 2010.
   Google Scholar