Combined Use of Cointegration Analysis and Robust Outlier Statistics to Improve Damage Detection in Real-World Structures

Abstract

Due to the need for controlling many ageing and complex structures, structural health monitoring (SHM) has become increasingly common over the past few decades. However, one of the main limitations for the implementation of continuous monitoring systems in real-world structures is the effect that benign influences, such as environmental and operational variations (EOVs), have on damage sensitive features. These fluctuations may mask malign changes caused by structural damages, resulting in false structural condition assessment. When damage identification is implemented as novelty detection due to the lack of known damage states, outliers may be part of the data set as the result of the benign and malign factors mentioned above. Thanks to the developments in the field of robust outlier detection, the current paper presents a new data fusion method based on the use of cointegration and minimum covariance determinant estimator (MCD), which allows us to visualize and to classify outliers in SHM data, depending on their origin. To validate the effectiveness of this technique, the recent case study of the KW51 bridge has been considered, whose natural frequencies are subjected to variations due to both EOVs and a real structural change.

Bridge Damage Detection Using Complexity Pursuit and Extreme Value Theory

“…Maes and Lombaert used acceleration data from the SHM system and the Operational Modal Analysis (OMA) method to determine the 1st to 14th modal frequencies of the bridge [42]. The bridge was considered to be in normal condition (NC) before retrofitting and in a damaged condition (DC) after retrofitting, respectively [43,44]. In this paper, as in the literature [43,44], the 6th, 10th, 12th, and 13th-order modal frequencies are chosen for damage detection.…”

“…The bridge was considered to be in normal condition (NC) before retrofitting and in a damaged condition (DC) after retrofitting, respectively [43,44]. In this paper, as in the literature [43,44], the 6th, 10th, 12th, and 13th-order modal frequencies are chosen for damage detection. These modes correspond to the 2nd to 5th vertical vibration modes of the bridge.…”