Potential of neural networks for maximum displacement predictions in
railway beams on frictionally damped foundations
Abstract
Since the use of finite element (FE) simulations for the dynamic
analysis of railway beams on frictionally damped foundations are (i)
very time consuming, and (ii) require advanced know-how and software
that go beyond the available resources of typical civil engineering
firms, this paper aims to demonstrate the potential of Artificial Neural
Networks (ANN) to effectively predict the maximum displacements and the
critical velocity in railway beams under moving loads. Four ANN-based
models are proposed, one per load velocity range ([50, 175] ∪
[250, 300] m/s; ]175, 250[ m/s) and per displacement type
(upward or downward). Each model is function of two independent
variables, a frictional parameter and the load velocity. Among all
models and the 663 data points used, a maximum error of 5.4 % was
obtained when comparing the ANN- and FE-based solutions. Whereas the
latter involves an average computing time per data point of thousands of
seconds, the former does not even need a millisecond. This study was an
important step towards the development of more versatile (i.e.,
including other types of input variables) ANN-based models for the same
type of problem.