Deterioration sensitive feature using enhanced AR Modelresiduals

An extensive number of already built buildings are deteriorating due to environmental effects, varying service loads, and aging. Hence, it is extremely crucial to accurately and continuously track the deterioration condition of these structures by employing some structural health monitoring (SHM) based assessment procedures. In this regard, vibration-based methods are amongst the most effective ones as they can be used in ambient vibration and operational loading conditions. Each building has unique vibration characteristics that will change due to accumulated deterioration and damage. However, the changes due to deterioration are generally subtler than changes due to damage, and consequently more difficult to detect. Therefore, deterioration detection procedures need to be more accurate and sensitive to these changes. This paper presents an autoregressive (AR) time-series residual-based deterioration assessment method which uses SHM data to capture changes in dynamic characteristics of building structures. A novel AR model order estimation procedure was proposed in order to enhance the sensitivity of the method. The result shows that the proposed methodology can clearly detect deterioration.