Open AccessPotential of neural networks for maximum displacement predictions in railway beams on frictionally damped foundations
Author(s) -
Miguel Abambres,
Rita Corrêa,
A. Pinto da Costa,
Fernando M. F. Simões
Publication year - 2019
Publication title -
avances en ciencias e ingenierías/avances en ciencias e ingenierías (en línea)
Language(s) - English
Resource type - Journals
eISSN - 2528-7788
pISSN - 1390-5384
DOI - 10.18272/aci.v11i2.1365
Subject(s) - artificial neural network , displacement (psychology) , range (aeronautics) , function (biology) , millisecond , point (geometry) , finite element method , structural engineering , type (biology) , computer science , engineering , mathematics , physics , geology , artificial intelligence , geometry , aerospace engineering , psychology , paleontology , astronomy , evolutionary biology , psychotherapist , biology
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.