Open Journal of Civil Engineering, 2016, 6, 31-41 Published Online February 2016 in SciRes. http://www.scirp.org/journal/ojce http://dx.doi.org/10.4236/ojce.2016.61004 How to cite this paper: Rusia, S. and Pathak, K.K. (2016) Application of Artificial Neural Network for Analysis of Triangular Plate with Hole Considering Different Geometrical and Loading Parameters. Open Journal of Civil Engineering, 6, 31-41. http://dx.doi.org/10.4236/ojce.2016.61004 Application of Artificial Neural Network for Analysis of Triangular Plate with Hole Considering Different Geometrical and Loading Parameters Saket Rusia, Krishna Kant Pathak Department of Civil and Environmental Engineering, National Institute of Technical Teachers’ Training and Research, Bhopal, India Received 6 February 2016; accepted 26 February 2016; published 29 February 2016 Copyright © 2016 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract In this study, Artificial Neural Network has been employed for analysis of triangular plate with different geometrical and loading parameters. Plates, having different sizes of concentric holes are analyzed. Finite element analysis for 81 cases is carried out using ANSYS Workbench 15.0 software. Using these data of FEM analysis an Artificial Neural Network has been trained. The successfully trained network is further used for analysis of four new cases which are also validated by using ANSYS Workbench 15.0 software. Keywords Artificial Neural Networks, Finite Element Analysis, Triangular Plate, ANSYS 1. Introduction Regardless of the powerful analysis software now available those allow us to find out the numerical solution of various problems, including problems of structural analysis, the development of methods of approximate solu- tion which would provide solutions in the form of simple analytic expressions is very important. One of the me- thods is artificial neural network also known as ANN. These are a functional abstraction of the biologic neural structures of the central nervous system. Scientists have long been inspired by the human brain. In 1943, Warren S. McCulloch, a neuroscientist, and Walter Pitts [1], a logician, developed the first conceptual model of an Artificial Neural Network. In their paper,