Artificial neural network calculations for a receding contact problem

Yaylaci E., Yaylaci M., ÖLMEZ H., BİRİNCİ A.

COMPUTERS AND CONCRETE, vol.25, no.6, pp.551-563, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 25 Issue: 6
  • Publication Date: 2020
  • Doi Number: 10.12989/cac.2020.25.6.551
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.551-563
  • Keywords: contact problem, artificial neural network, quarter plane, multilayer perceptron, theory of elasticity, COMPRESSIVE STRENGTH, CONCRETE, PREDICTION, BEAMS, PERFORMANCE, REGRESSION, MODELS
  • Recep Tayyip Erdoğan University Affiliated: Yes


This paper investigates the artificial neural network (ANN) to predict the dimensionless parameters for the maximum contact pressures and contact areas of a contact problem. Firstly, the problem is formulated and solved theoretically by using Theory of Elasticity and Integral Transform Technique. Secondly, the contact problem has been extended based on the ANN. The multilayer perceptron (MLP) with three -layer was used to calculate the contact distances. External load, distance between the two quarter planes, layer heights and material properties were created by giving examples of different values were used at the training and test stages of ANN. Program code was rewritten in C++. Different types of network structures were used in the training process. The accuracy of the trained neural networks for the case was tested using 173 new data which were generated via theoretical solutions so as to determine the best network model. As a result, minimum deviation value (difference between theoretical and C++ ANN results) of was obtained for the network model. Theoretical results were compared with artificial neural network results and well agreements between them were achieved.