The performance of switches and crossings compared with plain line is complicated by the presence of movable parts, changing rail geometry and non-uniformities in the composite and/or trackbed stiffness. These features lead to complex vehicle–track interactions and higher maintenance costs. The trackbed stiffness is the least well-controlled engineering property. A greater variability in trackbed stiffness leads to higher differential trackbed settlement and associated poorer track quality. At switches and crossings, changes in trackbed stiffness are exacerbated by changing rail properties which also contribute to changes in the overall composite track stiffness. This work focuses on the role of variations in stiffness on the performance of switches and crossings. Field measurements of bearer displacement were carried out using geophones at a switch and crossing equipped with under sleeper pads. The vehicle–switch and crossing interaction was modelled using a multi-body system and finite element method. The trackbed stiffness along the whole of the switch and crossing was inferred using the measurements of track deflections in an iterative back-calculation taking account of changing rail properties. It is shown that not including the variation in trackbed/composite stiffness leads to significant under/overestimates of the wheel–rail contact forces. Under sleeper pads are shown to reduce absolute maximum loads, but may increase the variation in deflection.
With rapid advances in sensor and condition monitoring technologies, railways infrastructure managers are turning their attention towards the promises that digital information and big data will help them understand and manage their assets more efficiently. In addition to existing track geometry records, it is evident that track stiffness is a key physical quantity to help assess track quality and its long-term deterioration. The present paper analyses the role of the track stiffness and its spatial variability through a set of computational experiments, varying other vehicle and track physical quantities such as vehicle unsprung mass, speed and track vertical irregularities. The support stiffness conditions are obtained using a sample procedure from an Autoregressive Integrated Moving Average (ARIMA) model to generate representative larger set of data from previously on-site measured data. A set of computational experiments is carefully designed, varying different physical variables, and a vehicle-track interaction model is used to estimate track geometry deterioration rates. A series of log-linear regression models are then used to analyse the impact of the tested physical variables on the track deterioration. The main findings suggest that the spatial variability of track stiffness contributes significantly to the track deterioration rates, and thus it should be used in the future to better target design and maintenance of railway track. Finally, a comparative study of some settlement models available in literature shows that they are very dependent on the test conditions under which they have been derived.
The dynamic behavior at a rail joint is examined using a two-dimensional vehicle-track coupling model. The track system is described as a finite length beam resting on a double layer discrete viscous-elastic foundation. The vehicle is represented by a half car body and a single bogie. The influence of the number of track layers considered, the number of rail elements between two sleepers and the beam model type is investigated. Parametric studies both of the coupling model and the analytic formulae are carried out in order to understand the influence of the main track and vehicle parameters on the P1 and P2 peak forces. Finally, the results in terms of P2 force from the present model have been compared not only with measured values but also with other simulated and analytical solutions. An excellent agreement between simulated and measured values has been found and the variation with respect to analytical formulae has been quantified.
Purpose This paper reviews and adapts the methodology BGuide on the methodology for carrying out cost
Most existing models of driver steering control do not consider the driver's sensory dynamics, despite many aspects of human sensory perception having been researched extensively. The authors recently reported the development of a driver model that incorporates sensory transfer functions, noise and delays. The present paper reports the experimental identification and validation of this model. An experiment was carried out with five test subjects in a driving simulator, aiming to replicate a real-world driving scenario with no motion scaling. The results of this experiment are used to identify parameter values for the driver model, and the model is found to describe the results of the experiment well. Predicted steering angles match the linear component of measured results with an average 'variance accounted for' of 98% using separate parameter sets for each trial, and 93% with a single fixed parameter set. The identified parameter values are compared with results from the literature and are found to be physically plausible, supporting the hypothesis that driver steering control can be predicted using models of human perception and control mechanisms.
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