Substructural Identification for Damage Estimation of Structures

Abstract

A method of substructural identification is presented for the estimation of local damage in complex structural systems. For this purpose, an auto-regressive and moving average with stochastic input (ARMAX) model is derived for a substructure to process the measurement data impaired by noises. The sequential prediction error method is used for the estimation of unknown parameters related to damages. Using the substructural method, the number of unknown parameters for each identification can be significantly reduced, hence the convergence and accuracy of estimation can be improved. For some substructures, the effect of the input excitation is expressed in terms of the responses at the interfaces with the main structure, and substructural identification may be carried out without measuring the actual input excitation to the structure. Direct and indirect methods for estimation of the element damage indices are also developed for local damage assessments. Example analyses are carried out for idealized structural models of a multistory building and a truss bridge. The results indicate that the present method is effective and efficient for local damage estimation of complex structures. (C) 1997 Elsevier Science Ltd.