The Prediction of Buckling Load of Laminated Composite Hat-Stiffened Panels Under Compressive Loading by Using of Neural Networks



Shashi Kumar*, Rajesh Kumar, Sasankasekhar Mandal, Atul K. Rahul
Department of Civil Engineering, Indian Institute of Technology (BHU) Varanasi, India

Abstract

Background:

Stiffened panels are being used as a lightweight structure in aerospace, marine engineering and retrofitting of building and bridge structure. In this paper, two efficient analytical computational tools, namely, finite element analysis (FEA) and artificial neural network (ANN) are used to analyze and compare the results of the laminated composite 750-hat-stiffened panels.

Objective:

Finite element (FE) is an efficient and versatile method for the analysis of a complex problem. FE models have been used to generate data set of four different parameters. The four parameters are extensional stiffness ratio of skin in the longitudinal direction to the transverse direction, orthotropy ratio of the panel, the ratio of twisting stiffness to transverse flexural stiffness and smeared extensional stiffness ratio of stiffeners to that of the plate.

Results and Conclusion:

For training of ANN, multilayer feedforward back-propagation has been used as a network function with two-hidden layers in the neural network. The good network architecture is achieved after several iterations to predict the buckling load of the stiffened panel. ANN prediction for unknown new data set is in good agreement with FEA results of different cases, which show that ANN tool can be used for the design of complex structural problems in civil engineering and optimization of the laminated composite stiffened panel.

Keywords: ANN, Buckling Analysis, Composite Panel, Compressive Loading, Fiber-reinforced polymers (FRP), Hat-stiffener.


Abstract Information


Identifiers and Pagination:

Year: 2018
Volume: 12
Publisher Item Identifier: EA-TOCIEJ-2018-51

Article History:

Received Date: 18/8/2018
Revision Received Date: 25/10/2018
Acceptance Date: 5/11/2018
Electronic publication date: 22/11/2018
Collection year: 2018

© 2018 Kumar et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


* Address correspondence to this author at the Department of Civil Engineering, Indian Institute of Technology (BHU) Varanasi, India; Tel: +91-8307847691; E-mail: shashi.rs.civ14@iitbhu.ac.in