Railway turnouts are elements exposed to an accelerated track degradation when compared to plain line track.
The particular and complex wheel/rail contact characteristics in railway turnouts make the contact forces acting on the wheel/rail interface particularly high. As a consequence of the train/track interaction force magnitude, track degradation is more accelerated here, having a big impact on the general costs used for track maintenance. The contact phenomenon has a stochastic nature due to the great number of variables involved in the interaction problem. Moreover, the uncertainty levels associated with each of the aforementioned variables require in-depth stochastic analyses that reflect the real variability of the parameters which characterize the dynamic interaction and consequently the degradation problem at railway turnouts.
The scope of this work is to perform a probabilistic assessment of a railway switch and crossing, aided by the multi-body simulation software, GENSYS. The critical track components with a major impact on track degradation are identified by means of a sensitivity assessment based on the elementary effects method while the uncertainty is assessed by using the Monte Carlo simulation method. The proposed methodology used to evaluate the probabilistic problem is a suitable approach to identify the railpad stiffness, the mass of the bogie, the damping value of the primary suspension and the ballast stiffness, being the critical parameters with the highest impact in the uncertainty analysis of track degradation at turnouts.
Railway turnouts Multi-body simulation Probabilistic assessment Monte Carlo method
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Kassa E, Nielsen JC (2008) Dynamic interaction between train and railway turnout: full-scale field test and validation of simulation models. Veh Syst Dyn 46(S1):521–534CrossRefGoogle Scholar
Wang P, Xu J, Xie K, Chen R (2016) Numerical simulation of rail profiles evolution in the switch panel of a railway turnout. Wear 366:105–115CrossRefGoogle Scholar
Lagos RF, Alonso A, Vinolas J, Perez X (2012) Rail vehicle passing through a turnout: analysis of different turnout designs and wheel profiles. Proc Inst Mech Eng Part F J Rail Rapid Transit 226(6):587–602CrossRefGoogle Scholar
Kassa E, Andersson C, Nielsen JC (2006) Simulation of dynamic interaction between train and railway turnout. Veh Syst Dyn 44(3):247–258CrossRefGoogle Scholar
Schupp G, Weidemann C, Mauer L (2004) Modelling the contact between wheel and rail within multibody system simulation. Veh Syst Dyn 41(5):349–364CrossRefGoogle Scholar
Pålsson BA, Nielsen JC (2015) Dynamic vehicletrack interaction in switches and crossings and the influence of rail pad stiffnessfield measurements and validation of a simulation model. Veh Syst Dyn 53(6):734–755CrossRefGoogle Scholar
Li X, Nielsen JC, Pålsson BA (2014) Simulation of track settlement in railway turnouts. Veh Syst Dyn 52(sup1):421–439CrossRefGoogle Scholar
Kouroussis G, Florentin J, Verlinden O (2016) Ground vibrations induced by InterCity/InterRegion trains: a numerical prediction based on the multibody/finite element modeling approach. J Vib Control 22(20):4192–4210CrossRefGoogle Scholar
De Miguel A, Lau A, Santos I (2018) Numerical simulation of track settlements based on an iterative holistic approach. J Braz Soc Mech Sci Eng 40(8):381CrossRefGoogle Scholar
Sun YQ, Cole C, Spiryagin M, Dhanasekar M (2013) Vertical dynamic interaction of trains and rail steel bridges. Electron J Struct Eng 13(Special Issue):88–97Google Scholar
Rocha JM, Henriques AA, Cala̧da R (2014) Probabilistic safety assessment of a short span high-speed railway bridge. Eng Struct 71:99–111CrossRefGoogle Scholar
Wetzel C, Proppe C (2007) Probabilistic assessment of the crosswind stability of railway vehicles. In: Proceedings of Weimar Optimization and Stochastic Days 4Google Scholar
Jamshidi A, Roohi SF, Núñez A, Babuska R, De Schutter B, Dollevoet R, Li Z (2016) Probabilistic defect-based risk assessment approach for rail failures in railway infrastructure. IFAC-PapersOnLine 49(3):73–77CrossRefGoogle Scholar
Perrin G, Soize C, Duhamel D, Funfschilling C (2013) Track irregularities stochastic modeling. Probab Eng Mech 34:123–130CrossRefGoogle Scholar
Kassa E, Nielsen JC (2008) Stochastic analysis of dynamic interaction between train and railway turnout. Veh Syst Dyn 46(5):429–449CrossRefGoogle Scholar
Zhu M, Cheng X, Miao L, Sun X (2015) Random field modeling of track irregularity of Beijing-Guangzhou high-speed railway with Karhunen–Loeve expansion. Int J Distrib Sens Netw 11(6):521437CrossRefGoogle Scholar
Larivière J, Cogan S, Green PL, Foltte E, Ham-Livet G (2017) Sensitivity analysis of nonlinear railway vehicle models using linearized proxy analyses. In: Nonlinear dynamics, vol 1. Springer, Cham, pp 155–158CrossRefGoogle Scholar
Kraft S, Puel G, Aubry D, Funfschilling C (2013) Improved calibration of simulation models in railway dynamics: application of a parameter identification process to the multi-body model of a TGV train. Veh Syst Dyn 51(12):1938–1960CrossRefGoogle Scholar
Shinozuka M (1972) Monte Carlo solution of structural dynamics. Comput Struct 2(5–6):855–874CrossRefGoogle Scholar
Rubinstein RY, Kroese DP (2016) Simulation and the Monte Carlo method, vol 10. Wiley, HobokenCrossRefGoogle Scholar
Salcher P, Pradlwarter H, Adam C (2014) Reliability of high-speed railway bridges with respect to uncertain characteristics. In: Proceedings of the 9th international conference on structural dynamicsGoogle Scholar
Rocha JM, Henriques AA, Cala̧da R, Rönnquist A (2015) Efficient methodology for the probabilistic safety assessment of high-speed railway bridges. Eng Struct 101:138–149CrossRefGoogle Scholar
Quiroga LM, Schnieder E (2012) Monte Carlo simulation of railway track geometry deterioration and restoration. Proc Inst Mech Eng Part O J Risk Reliab 226(3):274–282Google Scholar
Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Tarantola S (2008) Global sensitivity analysis: the primer. Wiley, HobokenzbMATHGoogle Scholar
López-Pita A, Teixeira PF, Casas C, Bachiller A, Sanchez M (2006) Deterioration in Geometric Track Quality on High-Speed Lines. CENIT, StuggartGoogle Scholar
Lei X (2017) High speed railway track dynamics: models, algorithms and applications. Science Press, Beijing/Springer Nature Singapore Pte. Ltd.(Cited on pages 58, 46, 87) SpringerGoogle Scholar