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Smart Structures and Systems Volume 13, Number 5, May 2014 , pages 849-868 DOI: https://doi.org/10.12989/sss.2014.13.5.849 |
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Indirect structural health monitoring of a simplified laboratory-scale bridge model |
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Fernando Cerda, Siheng Chen, Jacobo Bielak, James H. Garrett,Piervincenzo Rizzo and Jelena Kovačević
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| Abstract | ||
| An indirect approach is explored for structural health bridge monitoring allowing for wide, yet cost-effective, bridge stock coverage. The detection capability of the approach is tested in a laboratory setting for three different reversible proxy types of damage scenarios: changes in the support conditions (rotational restraint), additional damping, and an added mass at the midspan. A set of frequency features is used in conjunction with a support vector machine classifier on data measured from a passing vehicle at the wheel and suspension levels, and directly from the bridge structure for comparison. For each type of damage, four levels of severity were explored. The results show that for each damage type, the classification accuracy based on data measured from the passing vehicle is, on average, as good as or better than the classification accuracy based on data measured from the bridge. Classification accuracy showed a steady trend for low (1-1.75 m/s) and high vehicle speeds (2-2.75 m/s), with a decrease of about 7% for the latter. These results show promise towards a highly mobile structural health bridge monitoring system for wide and cost-effective bridge stock coverage. | ||
| Key Words | ||
| indirect SHM; laboratory experiment; damage detection; classification | ||
| Address | ||
| Fernando Cerda : Universidad de Concepción, Concepción, Chile Siheng Chen: Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA Jacobo Bielak and James H. Garrett: Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA Piervincenzo Rizzo: Department of Civil and Environmental Engineering, University of Pittsburgh, PA 15261, USA Jelena Kovačević: Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA | ||