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Driving risk of road vehicle shielded by bridge tower under strong crosswind

  • Helu Yu
  • Bin WangEmail author
  • Yongle Li
  • Mingjin Zhang
Original Paper
  • 74 Downloads

Abstract

Moving on a bridge, a road vehicle will experience a gusty crosswind caused by the shielding effects of the bridge tower. The corresponding driving safety is affected by several uncertain factors. In this study, a closed-loop driver-vehicle-crosswind (DVC) model is established to obtain the dynamic responses of the vehicle under given driving conditions. The nonlinear tire cornering force, the real driver behavior and the vehicle aerodynamic loads considering the wind shielding effects of the bridge tower can be taken into account. Based on the support vector classifier approach, a probability-based procedure is established to assess the driving risk of a road vehicle passing by a bridge tower under crosswind. A typical two-axle road vehicle passing by a bridge tower is studied as a numerical case. The dynamic responses of the vehicle and driver reaction under two given driving conditions are analyzed by solving the DVC numerical model firstly. Then, the probability-based model is used to assess the driving risk of the vehicle under different driving conditions; the effects of vehicle speed, wind velocity and friction coefficient of road surface on the driving risk are investigated.

Keywords

Driving risk Road vehicle Crosswind Bridge tower 

Notes

Acknowledgements

The authors are grateful for the financial supports from the open project of the Transport Industry Key Laboratory for Wind Resistance Technique in Bridge Engineering (KLWRTBMC14-02), National Natural Science Foundation of China (51508480), the Fundamental Research Funds for the Central Universities (2682016CX018) and National Natural Science Foundation of China (51878579, 51525804).

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

References

  1. Abe M (2009) Vehicle handling dynamics-theory and application. Butterworth-Heinemann, OxfordGoogle Scholar
  2. Argentini T, Ozkan E, Rocchi D, Rosa L, Zasso A (2011) Cross-wind effects on a vehicle crossing the wake of a bridge pylon. J Wind Eng Ind Aerodyn 99:734–740CrossRefGoogle Scholar
  3. Baker CJ (1986) A simplified analysis of various types of wind-induced road vehicle accidents. J Wind Eng Ind Aerodyn 22:69–85CrossRefGoogle Scholar
  4. Baker CJ (1991) Ground vehicles in high cross winds. 1. Steady aerodynamic forces. J Fluids Struct 5:69–90CrossRefGoogle Scholar
  5. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2:121–167CrossRefGoogle Scholar
  6. Charuvisit S, Kimura K, Fujino Y (2004) Experimental and semi-analytical studies on the aerodynamic forces acting on a vehicle passing through the wake of a bridge tower in cross wind. J Wind Eng Ind Aerodyn 92:749–780CrossRefGoogle Scholar
  7. Cheli F, Belforte P, Melzi S, Sabbioni E, Tomasini G (2006) Numerical-experimental approach for evaluating cross-wind aerodynamic effects on heavy vehicles. Veh Syst Dyn 44:791–804CrossRefGoogle Scholar
  8. Chen SR, Cai CS (2004) Accident assessment of vehicles on long-span bridges in windy environments. J Wind Eng Ind Aerodyn 92:991–1024CrossRefGoogle Scholar
  9. Chen SR, Chen F (2010) Simulation-based assessment of vehicle safety behavior under hazardous driving conditions. J Transp Eng ASCE 136:304–315CrossRefGoogle Scholar
  10. Chen F, Chen SR (2011) Reliability-based assessment of vehicle safety in adverse driving conditions. Transp Res Part C Emerg Technol 19:156–168CrossRefGoogle Scholar
  11. Chin WH, Chih CC, Chih JL (2016) A practical guide to support vector classification. http://www.csie.ntu.edu.tw/~cjlin. Accessed 6 Mar 2018
  12. Chojaczyk AA, Teixeira AP, Neves LC, Cardoso JB, Soares CG (2015) Review and application of artificial neural networks models in reliability analysis of steel structures. Struct Saf 52:78–89CrossRefGoogle Scholar
  13. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297Google Scholar
  14. Crolla D, Yu F (2004) Vehicle dynamics and control. China Communication Press, Beijing (in chinese) Google Scholar
  15. Ding HT, Guo KH, Li F, Zhang JW (2010) Arbitrary path and speed following driver model based on vehicle acceleration feedback. J Mech Eng 46:116–120 (in Chinese) CrossRefGoogle Scholar
  16. Dodds CJ, Robson JD (1973) The description of road surface roughness. J Sound Vib 31:175–183CrossRefGoogle Scholar
  17. Dugoff H, Fancher PS, Segel L (1970) An analysis of tire traction properties and their influence on vehicle dynamic performance. SAE Pap 700377:25Google Scholar
  18. Guo K, Guan H (1993) Modeling of driver vehicle directional control-system. Veh Syst Dyn 22:141–184CrossRefGoogle Scholar
  19. Guo WH, Xu YL (2006) Safety analysis of moving road vehicles on a long bridge under crosswind. J Eng Mech ASCE 132:438–446CrossRefGoogle Scholar
  20. ISO (1995) Mechanical vibration-road surface profiles-reporting of measured data. ISO8068Google Scholar
  21. Ma L, Zhou DJ, Han WS, Wu J, Liu JX (2016) Transient aerodynamic forces of a vehicle passing through a bridge tower’s wake region in crosswind environment. Wind Struct 22:211–234CrossRefGoogle Scholar
  22. McKay MD, Beckman RJ, Conover WJ (1979) Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21:239–245Google Scholar
  23. Proppe C, Wetzel C (2010) A probabilistic approach for assessing the crosswind stability of ground vehicles. Veh Syst Dyn 48:411–428CrossRefGoogle Scholar
  24. Rocchi D, Rosa L, Sabbioni E, Sbrosi M, Belloli M (2012) A numerical-experimental methodology for simulating the aerodynamic forces acting on a moving vehicle passing through the wake of a bridge tower under cross wind. J Wind Eng Ind Aerodyn 104:256–265CrossRefGoogle Scholar
  25. Sigbjornsson R, Snaebjornsson JT (1998) Probabilistic assessment of wind related accidents of road vehicles: a reliability approach. J Wind Eng Ind Aerodyn 74–6:1079–1090CrossRefGoogle Scholar
  26. Snaebjornsson JT, Baker CJ, Sigbjornsson R (2007) Probabilistic assessment of road vehicle safety in windy environments. J Wind Eng Ind Aerodyn 95:1445–1462CrossRefGoogle Scholar
  27. Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10:988–999CrossRefGoogle Scholar
  28. Wang B, Xu YL (2015) Safety analysis of a road vehicle passing by a bridge tower under crosswinds. J Wind Eng Ind Aerodyn 137:25–36CrossRefGoogle Scholar
  29. Wang DL, Chen AR, Pang JB (2005) Wind speed criteria of driving safety of vehicles on cable-stayed bridges. In: Proceedings of the sixth Asia-Pacific conference on wind engineering, Seoul, KoreaGoogle Scholar
  30. Wang B, Xu YL, Zhu LD, Li YL (2014) Crosswind effect studies on road vehicle passing by bridge tower using computational fluid dynamics. Eng Appl Comput Fluid Mech 8:330–344Google Scholar
  31. Wang B, Xu YL, Li YL (2016) Nonlinear safety analysis of a running road vehicle under a sudden crosswind. J Transp Eng 142:12Google Scholar
  32. Wang B, Li YL, Yu HL, Liao HL (2017) Dynamic reliability evaluation of road vehicle subjected to turbulent crosswinds based on monte carlo simulation. Shock Vib. Article ID 2365812Google Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Bridge EngineeringSouthwest Jiaotong UniversityChengduChina

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