Abstract
This paper presents a structural damage assessment method that relies on a cross-correlation-based damage sensitive feature known as Inner Product Vector (IPV). Such a feature is extracted from the dynamic response measured at different locations within a structure. Previous investigations on the use of the IPV in a damage detection framework were conducted through laboratory tests by manipulating the excitation source in an input-output approach. In this paper, a new output-only approach has been developed to extract the Inner Product Vector only from the structural response time histories without knowledge of the input excitation. This is a more realistic scenario in structural health monitoring applications where it is difficult (in some cases, impossible) to monitor the external forces. The new estimation of the IPV is accomplished by taking the cross-correlations between filtered contributions to the overall structural response. It is shown how the elements of the IPV are affected by changes of the structural properties induced by damage and how effective the proposed approach is in the presence of external disturbances. The proposed approach has been proven to be very effective in dealing with dense sensor networks requiring large computational efforts. Numerical and experimental tests have been performed to address the reliability of the proposed damage index as a damage sensitive feature in an output-only framework.
Highlights
The objective of damage assessment is to evaluate the conditions of a specific structure by using the measurements of its response recorded by sensors strategically located on the structure itself [1]
The relation between the damage index vector {D, } and the normalized r′th mode shown in Eq (14), let us conclude that the elements of the former can be considered as local damage sensitive features and help us locate the damaged area(s)
From the original Inner Product Vector (IPV) formulation, this methodology relies on information extracted only from the dynamic response of the structure, without knowledge of the input excitation, e.g. in an output-only context
Summary
The objective of damage assessment is to evaluate the conditions of a specific structure by using the measurements of its response (either static or dynamic) recorded by sensors strategically located on the structure itself [1]. The dynamic response of the structure to a specific input excitation and/or external disturbances is used as the source from which to extract the necessary information In this framework, data collected from different sensors, e.g. acceleration time histories recorded by accelerometers, are generally processed to extract parameters that are indicative of the dynamic characteristics of a structure. Defining effective damage sensitive features is a challenging task because some of those features are application dependent, some are too sensitive to external (unaccountable) disturbances, etc To account for these uncertainties, some of the most recent works on pattern recognition in SHM has focused on statistical analysis (Worden et al [3, 4] and Balsamo and Betti [5]). The effectiveness of the proposed methodology has been validated by considering experimental results from a 3-DOF shear-type laboratory system
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