Localized damage detection of structures subject to multiple ambient excitations using two distance measures for autoregressive models

In this paper, the distance measures of autoregressive (AR) models are used as damage indicators. Two distance measures are discussed: one is the Itakura distance, and the other is the cepstral distance. The distance measures of AR model have been successfully applied in image, speech, and neurological signal processing applications. This research explores new applications of two distance measures for damage detection in civil engineering. A five-storey building model is used for performance verification. Verification simulations show efficiencies of both distance-based damage indicators when the excitations are mutually uncorrelated. However, the ability of damage indicators for damage localization is deteriorated when the multiple excitations are mutually correlated as there are strong correlations among them. In practice, the excitations acting on civil engineering structures are mutually dependent and correlated, such as wind and traffic loading. To overcome this difficulty, a pre-whitening filter is applied for removal of correlations in excitations before calculating the damage indicators. To examine the proposed methodology, simulation and experiment data have been tested. It can be concluded from the results that, by using the pre-whitening filter, the damage identification ability of the proposed damage indicators improves significantly, especially for damage localization. The damage indicators increase monotonically with damage severity, which provides the potential to damage quantification.