SHM Based Damage Detection Using Cointegration and Linear Multivariate Data Analysis: Performance Comparison Based on a Real Case Study

Abstract

Two alternative methodologies for online data normalization are described and compared: multiple linear regression followed by principal component analysis (MLRPCA) and cointegration (COI). While the former is being used for some time in the scope of SHM, only recently the latter was introduced to the analysis of SHM data. In both cases the statistical classification is performed resorting to the Hotelling T2 statistic. The developed algorithms are applied to a prestressed concrete cable-stayed bridge of which 3½ years of continuous data is available. Three performance indicators are used to compare the two methodologies: one is the number of false positives (incorrectly predicted damage events) and the other two are related to the sensitivity to damage. Several damage scenarios involving small section loss the stay-cables are simulated by corrupting the measured (real) time series with the structural response to the damage events obtained from a finite element model of the bridge. It is shown that both methodologies can provide robust results and reasonable sensitivity to damage.