Application of the EKE and LSE-UI Based Substructure Approach for Damage Detection with Limited Output Measurements

Abstract

This paper presents an effort to apply the EKE (extended Kalman estimator) and LSE-UI (least squares estimation for unknown input) technique to detect structures damage with limited output measurements. This technique can be extended to detect structural local damage in complex structures based on substructure approach. Structural parameters and the unknown inputs are identified by a recursive algorithm based on sequential application of the extended Kalman estimator for the extended state vector and the least squares estimation for the unknown inputs. Only a limited number of measured acceleration responses of the benchmark structure subject to unmeasured excitation inputs are utilized. This structural damage detection method is applied to the ASCE SHM benchmark building to test its efficacy and provide a solution to the complex case of the Phase I benchmark problem. Damage detection results indicate that the proposed technique can detect and localize structural damage of the complex benchmark problem with good accuracy.