A Substructure Approach for Damage Detection of Large Size Structures under Limited Input and Output Measurements

Recently, damage detection of large size structures based on the system identification, through the changes of structural dynamic parameters (mainly stiffness parameters), has attracted great attention. Due to practical limitations, it may not be possible to install enough sensors in the health monitoring system to measure either all the external excitations (inputs) or the responses (outputs) at all degree of freedoms. In this paper, a substructure approach for damage detection of large size structure is proposed. A large structure is decomposed into smaller substructures based on its finite element formulation. Interaction effect between adjacent substructures is accounted by considering the interaction forces at substructural interfaces as the `unknown inputs' to the substructures concerned. Two cases that measurements at the substructure interfaces are available or not available are considered. Base on sequential application of the extended Kalman estimator for the extended state vector and the least squares estimation for the unknown inputs, the approach can identify structural parameters, such as the stiffness, damping, and the unmeasured inputs in the substructures. Numerical simulation results of a 20 story shear building show that the proposed approach is capable of identifying structural parameters and unknown excitations with good accuracy. Thus, structural local damage can be detected through the degradation of the stiffness at structural element level.