抄録
Composite materials have been widely applied in a variety of engineering and industrial fields over the past two decades. In order to reduce maintenance costs and to avoid potential catastrophic failure, it is essential to detect damage to these composite structures at the earliest possible stage. Evolutionary approaches, such as genetic algorithms and simulated annealing, have attracted considerable attention as a means of solving a range of combinatorial optimization problems. Accordingly, the current study applies a hybrid evolutionary approach to detect damage in a simply supported equal-sided sector of a spherical laminate shell on the basis of natural frequency and mode shape data obtained from modal testing. The simulations consider two different damage scenarios, namely single-point damage and multiple-point damage. Furthermore, to simulate the measurement errors inherent in experimental modal testing, the simulations consider three specific error conditions, i.e. no error (the ideal case), a maximum 5% error in both the natural frequency and the mode shape data, and a maximum 5% error in the natural frequency data and a maximum 10% error in the mode shape data. The simulation results indicate that the algorithm successfully detects both the location and the extent of the damage in both damage scenarios irrespective of the magnitude of the errors assigned to the natural frequency and mode shape data.
