A Sub-Structural Damage Identification Technique Employing Craig-Bampton Sub-Structuring and Gray Wolf Optimizer

In this study, a method is proposed for damage identification of large-scale of two-dimensional moment resisting steel frames by utilizing Craig-Bampton sub-structuring and Gray Wolf Optimizer (GWO). Initially, an index based on the Discrete Wavelet Transform (DWT) of the first mode shape variation is employed to identify the potentially damaged substructures. Subsequently, the global structure is partitioned into damaged and undamaged substructures, making the modal analysis of each substructure more computationally efficient, easier, and less time-consuming. An iterative GWO procedure is then conducted at a lower computational cost due to the reduction in the size of the decision variable vector, which corresponds to the number of elements in the damaged substructures. To validate the proposed method, three damage scenarios are examined, involving multiple damages and incomplete mode shape measurements contaminated with a 10% noise level, as well as frequencies affected by a 5% noise level. After applying the proposed method, the stiffness reduction of the suspected substructure elements from the previous stage is estimated with high accuracy based on the results of the GWO.