Application of multi-objective optimization to structural damage estimation via model updating

This paper presents a novel damage detection method which simultaneously updates the undamaged as well as damaged structure model in a multi-objective optimization (MOO) process. Structural health monitoring via analysis of modal data and model updating has received considerable attention in the previous decade. Such damage detection methods typically require an updated baseline model of the undamaged structure and the associated errors can become aggregated when this baseline model is subsequently used for damage detection. The use of multi-objective model updating alleviates those issues. A beam structure with and without damage has been used as an example and different noise levels have been added to the identified mode shapes. The results have been compared with single-objective model updating and it has been found that the proposed method is more efficient for accurate estimation of damage severity.