Application of Artificial Neural Network to Analyze Hexagonal Plate with Hole Considering Different Geometrical and Loading Parameters

  • Saket Rusia Department of Civil and Environmental Engineering, National Institute of Technical Teachers’ Training and Research, Bhopal, MP, India
  • K. K. Pathak Department of Civil Engineering, Indian Institute of Technology, Varanasi (BHU), UP, India
Keywords: Artificial Neural Networks, Finite Element Analysis, Hexagonal Plate, ANSYS


In the competitive nature of structural engineering, industry and its heuristic problem-solving needs, among other reasons, have contributed to the development of some advanced decision making tools. A step ahead from soft computing techniques, now artificial intelligence has proved itself as an efficient tool for solving the problem in construction industry over old tedious analytical methods. In this study, Artificial Neural Network is compared with finite element techniques in order to find the stress, strain and deflection in plates with holes. For approaching the complexity of the problem, hexagonal plates with holes and different geometrical and loading parameters have been taken as specimens. Finite element analysis for 81 cases are carried out using the software based on finite element method (FEM), 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 FEM based software. It was found that most of the results were quite close to the FEM results. Such a technique can be used to reduce the computation time and labour.


Download data is not yet available.


[1] Adeli, H. Neural Networks in Civil Engineering. 1989–2000 Comput. Civ. Infrastruct. Eng. 16, 126-142.
[2] ANSYS. Workbench User’ s Guide. ANSYS Work. 15.0 15317, 724–746 (2013). Figure 9: Regression Analyses for Max Directional Deformation in -Z Direction between ANN and ANSYS for New Models.
[3] Baba, B. O. Buckling Behavior of Laminated Composite Plates. J. Reinf. Plast. Compos. 26, 1637–1655 (2007).
[4] Garrett, J. H. Where and why artificial neural networks are applicable in civil engineering. J. Comput. Civ. Eng. ASCE 8, 129–130 (1994).
[5] Nicholas, P. Emmanuel Padmanaban, K. P., Vasudevan, D. & Selvaraj, I. J. Neural network based buckling strength prediction of laminated composite plate with central cutout. Appl. Mech. Mater. 560 (2014).
[6] Paik, Jeom Kee, Ultimate strength of perforated steel plates under combined biaxial compression and edge shear loads. Thin-Walled Struct. 46, 207–213 (2008).
[7] Pan, Z., Cheng, Y. & Liu, J. Stress analysis of a finite plate with a rectangular hole subjected to uniaxial tension using modified stress functions. Int. J. Mech. Sci. 75, 265–277 (2013).
[8] Rusia, S. & Pathak, K. K. “Application of artificial neural network for analysis of triangular plate with hole considering different geometrical and loading parameters,” Open J. Civ. Eng., 06, 01, 31–41(2016).
[9] Singh, A. & Paul, U. Finite displacement static analysis of thin plate with an opening––a variational approach. Int. J. Solids Struct. 40, 4135–4151 (2003).
[10] Ukadgaonker, V. G. & Rao, D. K. N. A general solution for moments around holes in symmetric laminates. Compos. Struct. 49, 41–54 (2000).
[11] Wu, H. C. & Mu, B. On stress concentrations for isotropic/orthotropic plates and cylinders with a circular hole. Compos. Part B Eng. 34, 127–134 (2003).
How to Cite
Saket Rusia, & K. K. Pathak. (1). Application of Artificial Neural Network to Analyze Hexagonal Plate with Hole Considering Different Geometrical and Loading Parameters. Journal on Today’s Ideas - Tomorrow’s Technologies, 4(1), 1-13.