Development of artificial neural network techniques for prediction of wheel-rail forces
Over the past two decades, Artificial Neural Network (ANN) techniques have been used
in many fields of research, due to their better robustness and fail ure tolerance
capabilities compared with conventional modelling approaches. In applications such as
railway vehicle dynamics, discrete scenarios of vehicle/track interactions are currently
modelled using computer packages such as Simpack, ADAMS Rail, Vampire or
Medyna, which use multi-body techniques to accurately model different aspects of rail
vehicles and tracks such as derailment or passenger comfort.
This thesis presents novel ANN techniques which make it possible to achieve ANN
models accuracies comparable to those of multi-body techniques. A particular ANN
structure has been designed with the aim of simplifying the training of ANNs with long
training data sets. This is a Recurrent Neural Network (RNN) structure characterized by
an optimized feedback technique which requires very little computational power. This
novel structure and other more conventional RNN structures have been trained and
tested and the processing times compared. The efficiency of the novel ANN structure in
modelling a number of vehicle types, from passenger to friction damped freight vehicles,
has been validated against commonly used techniques and also with a newly designed
method, which consists of a combination of statistical functions applied to assess
different aspects of the ANN models responses.
The ANN designing and testing techniques have been implemented in a novel software
tool, developed by the author, oriented to the investigation of ANN techniques for nonlinear
The novel ANN structure appears to be much faster than other structures commonly
used for non-linear system modelling and adequate for the purposes of rail vehicle
Compared to conventional ANN validation techniques, such as the mean square error
and the cross-correlation function analysis, the novel assessment technique results in a
more accurate quantification of the error terms and therefore, in a safer assessment
which may be focused on aspects of the ANN model responses which are relevant in the
context of railway engineering.
The developed software tool is a significant contribution to the research by automating
most of the repetitive tasks involved in the investigation and therefore allowing a
considerable amount of ANNs to be designed and tested.