Centralized vs. Pattern-level Feature Extraction for Structural Damage Detection

The objective of this paper is to discuss two different categories on structural damage detection: One is the centralized feature extraction technique by using multi-sensor architecture and the other is the pattern-level data fusion by using the extracted features from individual sensor. The covariance-driven stochastic subspace identification and multivariate singular spectrum analysis are applied for the centralized feature extraction. For pattern-level feature recognition wavelet packet transforms and power spectral density from individual sensing record is used. Through both centralized and pattern-level feature extraction the results can not only detect the occurrence of structural damage but also can locate the damage. Finally, the dynamic finite element model updating is used for damage quantification. Verification of the proposed algorithms by using a research-scale bridge scouring test model to detect the damage occurrence and severity during scouring is presented. Finally, discussion on the computation effectiveness among different method is made.