Comparison of genetic programming with conventional methods for fatigue life modeling of FRP composite materials

Genetic programming is used in this paper for modeling the fatigue life of several fiber-reinforced composite material systems. It is shown that if the genetic programming tool is adequately trained, it can produce theoretical predictions that compare favorably with corresponding predictions by other, conventional methods for the interpretation of fatigue data. For the comparison of results, curves produced by the genetic programming tool are plotted together with curves produced by three other commonly used methods for the analysis of composite material fatigue data: linear regression, Whitneyps Weibull statistics and Sendeckyjps wear-out model. The modeling accuracy of this computational technique, whose application for this purpose is novel, is very high. The proposed modeling technique presents certain advantages compared to conventional methods. The new technique is a stochastic process that leads straight to a multi-slope S-N curve that follows the trend of the experimental data, without the need for any assumptions. [All rights reserved Elsevier].

[1]  Fernand Ellyin,et al.  Effect of stress ratio on the fatigue of unidirectional glass fibre/epoxy composite laminae , 1994 .

[2]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[3]  J. Whitney,et al.  Fatigue Characterization of Composite Materials , 1979 .

[4]  D. P. Almond,et al.  21 – A neural-network approach to fatigue-life prediction , 2003 .

[5]  Adrião Duarte Dória Neto,et al.  BUILDING OF CONSTANT LIFE DIAGRAMS OF FATIGUE USING ARTIFICIAL NEURAL NETWORKS , 2005 .

[6]  Efstratios F. Georgopoulos,et al.  Artificial neural networks in spectrum fatigue life prediction of composite materials , 2007 .

[7]  Darryl P Almond,et al.  The use of neural networks for the prediction of fatigue lives of composite materials , 1999 .

[8]  John F. Mandell,et al.  DOE/MSU composite material fatigue database: Test methods, materials, and analysis , 1997 .

[9]  J. H. Mitchell,et al.  Encyclopaedia of computer science and technology , 1978 .

[10]  M. A. Jarrah,et al.  Neuro-Fuzzy Modeling of Fatigue Life Prediction of Unidirectional Glass Fiber/Epoxy Composite Laminates , 2002 .

[11]  Bryan Harris,et al.  Fatigue in composites , 2003 .

[12]  Anastasios P. Vassilopoulos,et al.  Adaptive neuro-fuzzy inference system in modelling fatigue life of multidirectional composite laminates , 2008 .

[13]  Philip Clausen,et al.  An empirical model for fatigue behavior prediction of glass fibre-reinforced plastic composites for various stress ratios and test frequencies , 2003 .

[14]  Gerard Franklyn Fernando,et al.  Fatigue life prediction for hybrid composites , 1989 .

[15]  Julio F. Davalos,et al.  An artificial neural network for the fatigue study of bonded FRP–wood interfaces , 2006 .

[16]  Woonbong Hwang,et al.  Failure of carbon/epoxy composite tubes under combined axial and torsional loading 1. Experimental results and prediction of biaxial strength by the use of neural networks , 1999 .

[17]  Efstratios F. Georgopoulos,et al.  Modelling Fatigue Life of Multidirectional GFRP Laminates under Constant Amplitude Loading with Artificial Neural Networks , 2006 .

[18]  Yousef Al-Assaf,et al.  Fatigue life prediction of unidirectional glass fiber/epoxy composite laminae using neural networks , 2001 .

[19]  Anastasios P. Vassilopoulos,et al.  Complex stress state effect on fatigue life of GRP laminates. Part I, experimental , 2002 .