An experimental validation of a statistical-based damage detection approach.
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2011-01-01
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NTL Classification:NTL-HIGHWAY/ROAD TRANSPORTATION-Bridges and Structures;NTL-HIGHWAY/ROAD TRANSPORTATION-Construction and Maintenance;
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Abstract:In this work, a previously-developed, statistical-based, damage-detection approach was validated for its ability to
autonomously detect damage in bridges. The damage-detection approach uses statistical differences in the actual and
predicted behavior of the bridge caused under a subset of ambient trucks. The predicted behavior is derived from a
statistics-based model trained with field data from the undamaged bridge (not a finite element model). The differences
between actual and predicted responses, called residuals, are then used to construct control charts, which compare
undamaged and damaged structure data.
Validation of the damage-detection approach was achieved by using sacrificial specimens that were mounted to the bridge
and exposed to ambient traffic loads and which simulated actual damage-sensitive locations. Different damage types and
levels were introduced to the sacrificial specimens to study the sensitivity and applicability.
The damage-detection algorithm was able to identify damage, but it also had a high false-positive rate. An evaluation of
the sub-components of the damage-detection methodology and methods was completed for the purpose of improving the
approach. Several of the underlying assumptions within the algorithm were being violated, which was the source of the
false-positives. Furthermore, the lack of an automatic evaluation process was thought to potentially be an impediment to
widespread use. Recommendations for the improvement of the methodology were developed and preliminarily evaluated.
These recommendations are believed to improve the efficacy of the damage-detection approach.
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