An artificial neural network for the fatigue study of bonded FRP-wood interfaces

The objective of this study is to explore the development of an artificial neural network (ANN) method for the analysis of fatigue behavior of interface bonds for dissimilar materials. In particular,the application considered in this paper is for an assembly of a Fiber-Reinforced Polymer (FRP) composite adhesively bonded to wood;the composite laminate consists of E-glass unidirectional fiber bundles embedded in a phenolic resin (phenolic FRP),and the wood species is Red Maple. By constructing suitable network architectures, which can learn from existing experimental data sets and can provide output fatigue data sets for new input parameters, it is shown that the proposed neural network model is able to predict valuable fatigue responses, such as crack-growth rate,that would facilitate the development of design guidelines for hybrid material bonded interfaces.