Structural damage detection with limited input and output measurement signals

It is important but still challenging to detect structural damage with limited input and output measurement signals. In this paper, an algorithm is proposed for detecting structural damage with limited input and output measurement signals. The algorithm is based on sequential application of an extended Kalman estimator for the extended state vector of a structure and least-squares estimation of its unknown external excitations. Analytical recursive solutions for the identification of structural parameters and unknown excitations are derived. Such straightforward derivation and analytical solutions are not available in the previous literature. Structural damage can be detected from the degradation of the identified element stiffness. Numerical examples of detecting damage of some small size structural systems are used to demonstrate the performances of the proposed algorithm. Then, the algorithm is extended to detect structural damage of large size structural systems based on substructure approach. Inter-connection effect between adjacent substructures is considered by ‘additional unknown inputs’ to substructures. It is shown that the ‘additional unknown inputs’ can be estimated by the algorithm without the measurements of the substructure interface DOFs, which is superior to previous identification approaches. A numerical example of detecting structural damage of a large size truss illustrates the efficiency of the proposed algorithm.

► Detect structural damage with limited measured input and output signals.
► Sequential application of the extended Kalman estimator and least-squares estimation.
► Simplifies the identification problem compared with other algorithms available.
► Detect local damage of large size linear structural systems.
► Identification without the measurement signals at the substructure interface DOFs.