Using incomplete modal data for damage detection in offshore jacket structures

Abstract

The development of robust techniques for early damage detection for offshore structures is crucial to avoid the possible catastrophe caused by structural failures. This article applies the cross-model cross-mode (CMCM) method for damage detection that is capable of identifying the damage to individual members of offshore jacket platforms, when limited, spatially incomplete modal data is available. Basically, the CMCM method is classified as a direct, physical property adjustment model updating method. Implementing this method requires only a few modes measured from the damaged structure. In dealing with spatial incompleteness, this paper investigates both model reduction and modal expansion techniques. Specifically, either Guyan (static condensation) or SEREP (system equivalent reduction expansion process) transformation matrix, between the master and slave degrees-of-freedom, is employed in the model reduction or modal expansion process. One theoretical development is an iterative procedure to compute the transformation matrix associated with the (unknown) damaged structure. Numerical studies are conducted for a jacket platform with multiple damaged members based on synthetic data generated from finite-element models. The results suggest that (i) Guyan scheme always outperforms SEREP, (ii) model reduction is always better than modal expansion, and (iii) the CMCM method in conjunction with iterative Guyan reduction approach yields the best damage location and severity estimate.