Damage detection of structures using a modified genetic algorithm

Genetic algorithms (GA) have proved to be a robust, efficient search technique for many problems. As the number of variables to be determined increases however classical GA will often have difficulty and/or require large time in obtaining cceptable results. In this keynote paper, a GA strategy involving a search space reduction method (SSRM) using a modified genetic algorithm based on migration and artificial selection (MGAMAS) is presented to identify damaged members in multiple degree of freedom systems. The SSRM strategy is able to quickly and accurately identify structural parameters from the measured dynamic response of the structure. The SSRIVI is incorporated into a damage detection strategy whereby changes in the stiffness of the structure are identified using data obtained before and after damage has taken place. The additional information obtained from the analysis of the undamaged structure is used to greatly improve identification of the damaged structure. Numerical studies on 10 and 20 degree-of-freedom systems are presented to illustrate the ability of the method in accurately identifying even small amounts of damage. Performance is assessed in terms of the accuracy of identification of damage magnitude, the maximum damage falsely identified at an undamaged location and an identification success based on the ratio of the damage and false damage. Damage represented by a small change of stiffness of only 2.5% is accurately and consistently identified in the presence of 5% input and output noise.