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Statistical Modeling Methods for Structural Damage Identification

Early damage detection is very important for in-time repair and retrofit of civil structures. The past years have seen a growing interest in automatic/programmable damage detection techniques using measurements from structural monitoring because of its potential economic benefit for maintenance practices. Recently, statistical pattern recognition (SPC) has been brought forward as a promising way to realize this goal. The essence of SPC is to use well-defined concepts in statistics for data classification, which is an intuitive way to account for the inherent uncertainty in the data acquisition process. In this paper, two types of statistical damage detection algorithm are described and applied to the acceleration responses collected from a scaled two-bay steel frame subjected to random excitation. Damage is introduced into this specimen by replacing two members near the right joint with more flexible ones. The influence coefficient method for damage detection is based on correlation analysis between responses measured at two different locations, while the autoregressive (AR) modeling method evaluates the change in coefficients and residual characteristics of the AR model estimated from response from only one sensor node. Influence coefficients here are basically linear regression coefficients of one node’s response on another, and AR model, as indicated by its name, regress the current response on those from several time steps back. Both techniques have successfully identified the damage existence in the steel frame. It is shown that the amount of change occurred in the influence coefficient values from different node combinations can be used to localize damage. For the AR modeling method, the autocorrelation function of residuals at each sensing unit is proved to be a more effective damage location indicator than Mahalanobis distance of model coefficients at each sensing unit in this case.