Crushing behavior of laterally compressed composite elliptical tubes: Experiments and predictions using artificial neural networks El-Sadig Mahdi, Hany El Kadi * Mechanical Engineering Department, American University of Sharjah, Sharjah, United Arab Emirates Available online 7 June 2007 Abstract Composite materials have been increasingly used in the automobile industry for weight saving and part integration purposes. In this regard, composite elliptical tubes have been effectively employed as energy absorber devices. This increases the need for accurate and simple prediction techniques to optimize these structures. The present work deals with the implementation of artificial neural networks (ANN) technique in the prediction of the crushing behavior and energy absorption characteristics of laterally loaded glass fiber/epoxy composite elliptical tubes. Predicted results are com- pared with actual experimental data in terms of load carrying capacity and energy absorption capability showing good agreement. This shows that ANN techniques could effectively be used to predict the response of collapsible composite energy absorber devices subjected to different loading conditions. As is the case for experimental findings, the predictions obtained using ANN also show the significant effect of the ellipticity ratio on the crushing behavior of laterally loaded tubes. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Composite tubes; Ellipticity ratio; Lateral compression; Crushing behavior; Artificial neural networks 1. Introduction During the last century, the use of advanced materials in the design of energy absorber devices has been hampered by a lack of experimental and numerical simulation work that would guide designers to optimum energy absorber devices. Broad exploitation of advanced composites in energy absorber design will depend to a large degree on finding a reliable technique to predict their response to dif- ferent loading conditions. Up-to-date, most of the numer- ical prediction results in the field of composite structures crashworthiness have been obtained using finite element analysis [1–3]. Recently, artificial neural networks tech- nique was successfully implemented in composite damage detection field. This paper introduces the use of ANN in the field of collapsible energy-absorbing devices. The quasi-static crushing behavior of composite tubes has been studied both experimentally and numerically using finite elements analysis [1–10]. The load was either applied to the tube in an axial direction or in a transverse direction. In these studies, both the load carrying capacity and the energy absorption capability of composite tubes were investigated. The behavior of axially-loaded compos- ite elliptical tubes under compression loading has recently been investigated both experimentally and numerically [11]. In this study, the effect of the ellipticity ratio (a/b) on the load carrying capacity of the tubes as well as the energy absorption until failure were also investigated. LUSAS finite elements package was used for the numerical investigation of elliptic tubes with ellipticity ratios varying from 1 to 2 (1 signifying a circular tube). Although good agreement was obtained from the finite element analysis compared to the experimental results, the authors empha- sized that typical imperfections existing in the manufactur- ing process of the tubes could not possibly be accounted for by the finite element analysis. They suggested using a 0263-8223/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.compstruct.2007.05.009 * Corresponding author. E-mail address: hkadi@aus.edu (H. El Kadi). www.elsevier.com/locate/compstruct Available online at www.sciencedirect.com Composite Structures 83 (2008) 399–412