Benefits of an Implicit Redundant Genetic Algorithm Method for Structural Damage Detection in Noisy Environments

In this research, the problem of structural damage detection using noisy frequency response function information is addressed. A methodology for damage detection is proposed that uses an unconstrained optimization problem formulation. To solve the optimization problem genetic algorithms (GA) and a local hillclimbing procedure were used. The inherent unstructured nature of damage detection problems is exploited through the application of an implicit redundant representation (IRR) allowing for the number of decision variables to dynamically change during the course of optimization. To evaluate the proposed damage detection method, test runs for a cantilever beam and an unbraced frame structure were performed. Test case results using different measurement noise levels show that the IRR GA has superior performance over the standard GA fixed representation.