Structural damage estimation using dynamic data and multi-objective optimization

A novel damage estimation method is proposed using a multi-objective optimization technique which simultaneously updates the damaged as well as undamaged structural model. Contemporary damage detection and estimation methods based on model updating typically require a prior updated baseline finite element model of the undamaged structure for subsequent updating of the damaged model. However, multi-objective optimization algorithms utilize both the undamaged and damaged structure models concurrently to improve the performance of the damage estimation procedure. An updated solution is selected which gives a trade-off between updating of both models using the concept of Pareto front. The technique was applied to a numerically simulated structure with damage, and natural frequencies and mode shape information was used for model updating. Different noise levels were added to account for the experimental errors. It was found that the proposed method gives more accurate damage location and estimation than traditional updating, and is less sensitive to experimental errors.