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Early Damage Detection Based on Pattern Recognition and Data Fusion
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DOI：10.1061/(ASCE)ST.1943-541X.0001643.png)
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Abstract
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Structural health monitoring (SHM) relies on data acquired from sensorial systems installed on site, and is nowadays being used more often not only for asset management, but also in critical structures when there is the need to detect damage in an early stage, before it impairs structural performance and safety. Early detection of damage in critical structures relies on the acquisition of continuous streams of information and on reliable techniques capable of analyzing it in real time, without generating false alerts. In this context, the combination of data fusion strategies, capable of converting large amounts of data into small pieces of information, with pattern recognition algorithms, which are able to analyze this information in real time, is addressed in the present paper with the objective of developing an original strategy capable of (1)removing the effects of regular actions imposed to structures without the need to measure them and of (2)compressing entire SHM data sets of arbitrary dimensions into a sensitive single-valued damage index. These capabilities are achieved by combining principal component analysis, the broke-stick rule, clustering methods, symbolic data objects, and symbolic distances. The proposed strategy was tested and validated with a numerical model of a cable-stayed bridge, using experimental data as input. From this analysis it was observed that, under the noise levels measured on site, the proposed strategy is able to automatically detect damage as small as 1% of stiffness reduction in a single stay cable. This sensitivity evidenced by the proposed strategy can be considered particularly high because it was obtained from a small amount of inexpensive sensors with a static character and because it was associated with a false detection incidence of only 0.1%. (C) 2016 American Society of Civil Engineers.
Keywords:
Early damage detection
Principal component analysis
Symbolic data
Symbolic dissimilarity measures
Cluster analysis
Moving time windows
Single-valued damage index
Numerical model
Structural health monitoring
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