Automated Damage Index Estimation of Reinforced Concrete Columns for Post-Earthquake Evaluations
Publication: Journal of Structural Engineering
Volume 141, Issue 9
Abstract
The safety of buildings which have been affected by a natural disaster such as an earthquake is currently evaluated manually by certified inspectors who identify visible damage on the structural elements. This process has been proven to be time-consuming, costly, and can delay the response and recovery. In order to automate this type of assessment, it is necessary to automatically determine the damage state of the individual structural members. Previously, the writers have created novel methods for automated detection of RC columns as well as significant damage on the column surfaces (spalling and cracking). This paper presents a novel method of automatically determining the damage state of RC columns in RC frame buildings based only on the automatically detected damage and column information. In addition, the novel method automatically determines an associated engineering demand parameter, residual drift capacity. All of the methods previously developed by the writers are combined with the method presented in this paper and the results are compared with those of manual assessment procedures. The method was implemented using computer software and its effectiveness was confirmed with this comparison.
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Acknowledgments
The research reported in this paper is based in part upon work supported by the National Science Foundation under Grant Nos. CMMI-1034845 and CMMI-0738417. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the writers, and do not necessarily reflect the views of the National Science Foundation.
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Information
Published In
Journal of Structural Engineering
Volume 141 • Issue 9 • September 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Feb 24, 2014
Accepted: Oct 3, 2014
Published online: Nov 20, 2014
Discussion open until: Apr 20, 2015
Published in print: Sep 1, 2015
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