Damage detection in offshore jacket structures using incomplete modal data

摘要

The development of robust techniques for early damage detection and localization for offshore structures is crucial to avoid the possible catastrophe caused by structural failures.This paper employs the cross-model cross-mode(CMCM)method for the damage detection in offshore jacket-type platform when only one or two spatially incomplete modes are available.In practice,measured modes are commonly spatially incomplete.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,and 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 localization and severity estimate.