Matrix Crack Detection in Thin-walled Composite Beam using Genetic Fuzzy System

Since thin-walled composite structures are widely used in structural engineering, damage in such structures is an important issue of research. Matrix cracking is a principal cause of failure in composites. In the present study, a composite matrix cracking model is implemented in a thin-walled hollow circular cantilever beam using an effective stiffness approach. Such structures are used to model connecting shafts and helicopter tail boom, for example, because of their high stiffness-to-weight ratios and excellent crashworthiness characteristics. The effect of variation in crack density on the fundamental frequency, for various combinations of 1⁄2 m=90n s composite is studied. Using these change in frequencies due to matrix cracking, a genetic fuzzy system for crack density and crack location detection is generated. The genetic fuzzy system combines the uncertainty representation characteristics of fuzzy logic with the learning ability of genetic algorithm. It is observed that the success rate of the genetic fuzzy system in the presence of noise is dependent on crack density (level of damage), number of 90 plies, angle of constraining layer ( ), and noise level. It is found that the genetic fuzzy system shows excellent damage detection and isolation performance, and is robust to presence of noise in data.