Autoregressive statistical pattern recognition algorithms for damage detection in civil structures

Statistical pattern recognition has recently emerged as a promising set of complementary methods to system identification for automatic structural damage assessment. Its essence is to use well-known concepts in statistics for boundary definition of different pattern classes, such as those for damaged and undamaged structures. In this paper, several statistical pattern recognition algorithms using autoregressive models, including statistical control charts and hypothesis testing, are reviewed as potentially competitive damage detection techniques. To enhance the performance of statistical methods, new feature extraction techniques using model spectra and residual autocorrelation, together with resampling-based threshold construction methods, are proposed. Subsequently, simulated acceleration data from a multi degree-of-freedom system is generated to test and compare the efficiency of the existing and proposed algorithms. Data from laboratory experiments conducted on a truss and a large-scale bridge slab model are then used to further validate the damage detection methods and demonstrate the superior performance of proposed algorithms.

► Sensitivity analysis of damage features from autoregressive modeling is conducted. ► Statistical pattern recognition is used for damage detection algorithm formulation. ► Data-driven damage threshold construction is adopted. ► The damage detection methods are validated with data from a large-scale bridge slab. ► The performance of multiple damage detection algorithms are compared and contrasted.