Issued also as thesis, Columbia University. / Bibliography: p. 293-298.

Thesis (doctoral)--Delft University of Technology, 2007. / "Delft Centre for Sustainable Urban Areas"--Cover. "Erratum" inserted. Includes bibliographical references (p. 235-248).

Thesis (Ph. D.)--Ohio State University, 1991. / Includes bibliographical references (leaves 493-529).

Thesis (Ph. D.)--Columbia University, 1915. / Vita. Published also as Studies in history, economics and public law, ed. by the Faculty of political science of Columbia university, vol. LXI, no. 1, whole no. 148.

Thesis (M.Int.(Prof.))--University of Pretoria, 2005. / Includes summary. Includes bibliography. Available on the Internet via the World Wide Web.

Thesis (M.A.)--University of Texas at El Paso, 2006. / Includes bibliographical references (p. 113-134)

The aim of this thesis is to develop an integrated methodology for investigating the potential for new local railway stations within a given area, with particular emphasis on the use of Geographical Information Systems (GIS). Existing methods for assessing the case for constructing new local railway stations have often been found wanting, with the forecasts produced proving to be inaccurate. A review of previous work in this field has been undertaken and methodologies with the potential to enhance local rail demand models have been identified. Trip rate and trip end models have been developed which are capable of forecasting usage at new station sites anywhere in England and Wales. Geographically Weighted Regression (GWR) has been used to enhance the performance of these models and to account for local variations in the effects of explanatory variables on rail demand. Flow level models have been produced for stations in South-East Wales, with a range of model formulations tested. A survey of ultimate passenger trip origins and destinations was carried out in the same area, enabling the accuracy of theoretical station catchment definition methods to be tested. A GIS-based procedure for locating potential sites for new railway stations within a given area has been developed. This was combined with the results from the demand models and estimates of associated costs and benefits to give a synthesised appraisal procedure capable of assessing the case for constructing particular stations. This procedure was applied to 14 sites in South-East Wales and, along with trip end forecasts for 421 sites across the country, this indicated that there is almost certainly a positive case for constructing a significant number of new railway stations in the UK.

Pandrol Rail Fastenings Limited are a designer and manufacturer of railway rail-fastening systems. As an organisation they have the capability to reduce the noise impact of bridges using resilient track components. They also have a commercial interest in providing such technology. Knowledge of the processes behind bridge noise is important to Pandrol in two ways; to aid the engineers within the organisation in the design of fastening systems and to demonstrate a state-of-the-art understanding of the problem of railway bridge noise to customers, as this will aid in the sale of Pandrol products. The fitting of new rail components to an existing track form, or failure to meet noise regulations with a new track form, can be costly. It is important to be able to predict accurately the effectiveness of noise reduction techniques. Currently, Pandrol’s knowledge of the problem consists almost entirely of experience gained and data gathered while working on existing bridge projects. To expand their knowledge base, Pandrol perform noise and vibration measurements on railway bridges and viaducts and then use the measured data to predict the performance of their systems on other bridges. This completely empirical approach to predicting bridge noise is both costly and situation specific results cannot be provided before the installation of the fastening system. ii Another approach to predicting bridge noise is through the application of analytical models. Limited analytical modelling in the context of bridge noise is currently conducted within the organisation. For these reasons, Pandrol are sponsoring research into bridge noise in the form of this EngD project. Here an existing rapid calculation approach is identified that relies less on the exact geometry of the bridge and more on its general characteristics. In this approach an analytical model of the track is coupled to a statistical energy analysis (SEA) model of the bridge. This approach forms a suitable basis from which to develop a better model here by concentrating on its weaknesses. A mid-frequency calculation for the power input to the bridge via a resilient track system has been developed by modelling the track-bridge system as two finite Timoshenko beams continuously connected by a resilient layer. This has resulted in a power input calculation which includes the important effects of coupling between the rail and bridge and the resonance effects of the finite length of a bridge. In addition, a detailed study of the frequency characteristics of deep I-section beams has been performed using Finite Element, Boundary Element and Dynamic stiffness models. It is shown that, at high frequencies, the behaviour of the beam is characterised by in-plane motion of the beam web and bending motion in the flange. This knowledge has resulted in an improved calculation for the mobility of a bridge at high frequencies. The above improvements are included in an improved model for use by Pandrol in their general activities. Data from real bridges is compared to predictions from the improved model in order to validate different aspects of the model. The model is then used to study the effect on noise of varying many bridge design parameters. It is shown that the parameter that has most influence on the noise performance of a bridge is the dynamic stiffness of the resilient rail fastening system. Additionally it is demonstrated that for a given bridge and noise receiver location, an optimum fastener stiffness exists where the noise radiated by the bridge and track is at a minimum.

The noise level associated with a train travelling on a bridge is normally greater than that for a train travelling on plain track. It is sometimes the bridge noise that causes the highest levels of disturbance to people in the vicinity or triggers action under regulations such as the Environmental Noise Directive. Consequently, there is a need to study means of predicting noise levels from proposed bridges, noise control measures for existing structures and principles of low-noise bridge design. This thesis describes a programme of work in which an existing calculation model for bridge noise and vibration has been tested and alternative calculation methods have been developed where required. The existing model is based on analytical models for wheel-rail interaction and the calculation of the power input to the bridge. The response of the various component parts of the bridge for this power input is found using a simplified SEA scheme. In this work, the existing model has been tested against measurements made on railway bridges and the results of an advanced method of structural analysis, the Waveguide Finite Element (WFE) method. This method is well-suited to modelling some important types of railway bridge. Specifically, it allows a numerical modelling approach to be used up to higher frequency than conventional Finite Element methods. It has been found to offer some significant advantages over the existing bridge noise model, particularly for concrete-steel composite bridges and concrete box-section viaducts. The track support structure has an important influence on bridge noise and vibration, through its role in the transmission of vibration from the rail to the bridge. Laboratory measurements have been made in this work to characterise the vibration transmission properties of two important types of track support structure on bridges; ballasted track and two-stage resilient baseplate track. Improved methods of modelling the dynamic behaviour of these track forms have been developed from the measurements, which can be used in calculation models for both bridge noise and also for rolling noise.

The prediction of wheel wear is a significant issue in railway vehicles. It is correlated with safety against derailment, economy, ride comfort, and planning of maintenance interventions, and it can result in delay, and costs if it is not predicted and controlled in an effective way. However, the prediction of wheel and rail wear is still a great challenge for railway systems. Therefore, the main aim of this thesis is to develop a method for predicting wheel wear using artificial neural networks. Initial tests were carried out using a pin-on-disc machine and this data was used to establish how wear can be measured using an Alicona profilometer. A new method has been developed for detailed wheel wear and rail wear measurements using ‘Replica’ material which was applied to the wheel and rail surfaces of the test rig to make a copy of both surfaces. The replica samples were scanned using an optical profilometer and the results were processed to establish wheel wear and rail wear. The effect of load, and yaw angle on wheel wear and rail wear were examined. The effect of dry, wet, lubricated, and sanded conditions on wheel wear and rail wear were also investigated. A Nonlinear Autoregressive model with eXogenous input neural network (NARXNN) was developed to predict the wheel and rail wear for the twin disc rig experiments. The NARXNN was used to predict wheel wear and rail wear under deferent surface conditions such as dry, wet, lubricated, and sanded conditions. The neural network model was developed to predict wheel wear in case of changing parameters such as speed and suspension parameters. VAMPIRE vehicle dynamic software was used to produce the vehicle performance data to train, validate, and test the neural network. Three types of neural network were developed to predict the wheel wear: NARXNN, backpropagation neural network (BPNN), and radial basis function neural network (RBFNN). The wheel wear was calculated using an energy dissipation approach and contact position on straight track. The work is focused on wheel wear and the neural network prediction of rail wear was only carried out in connection with the twin disk wear tests. This thesis examines the effect of neural network parameters such as spread, goal, maximum number of neurons, and number of neurons to add between displays on wheel wear prediction. The neural network simulation results were implemented using the Matlab program. The percentage error for wheel and rail wear prediction was calculated. Also, the accuracy of wheel and rail wear prediction using the neural network was investigated and assessed in terms of mean absolute percentage error (MAPE). The results reveal that the neural network can be used efficiently to predict wheel and rail wear. Further work could include rail wear and prediction on a curved track.