Influence of the Autoregressive Model Order on Damage Detection

An important step for using time-series autoregressive (AR) models for structural health monitoring is the estimation of the appropriate model order. To obtain an optimal AR model order for such processes, this article presents and discusses four techniques based on Akaike information criterion, partial autocorrelation function, root mean squared error, and singular value decomposition. A unique contribution of this work is to provide a comparative study with three different AR models that is carried out to understand the influence of the model order on the damage detection process in the presence of simulated operational and environmental variability. A three-story base-excited frame structure was used as a test bed in a laboratory setting, and data sets were measured for several structural state conditions. Damage was introduced by a bumper mechanism that induces a repetitive impact-type nonlinearity. The operational and environmental effects were simulated by adding mass and by changing the stiffness properties of the columns. It was found that these four techniques do not converge to a unique solution, rather all require somewhat qualitative interpretation to define the optimal model order. The comparative study carried out on these data sets shows that the AR model order range defined by the four techniques provides robust damage detection in the presence of simulated operational and environmental variability.