Automated Crack Detection and Damage Index Calculation for RC Structures Using Image Analysis and Fractal Dimension
Publication: Journal of Structural Engineering
Volume 147, Issue 4
Abstract
Damage assessment in postearthquake reconnaissance of civil structures has traditionally relied upon the judgment of experienced site inspectors. In a step toward eliminating the need for on-site inspections, this paper presents a method for automatic postdisaster structural damage assessment of reinforced concrete (RC) structures. The method uses digital image correlation to automatically detect cracks in RC structural elements and correlates the analyzed crack distributions to a damage index using fractal dimension. The method is applied in this study to track the progression of damage in a planar RC shear wall and a cylindrical RC containment vessel tested in a laboratory under reversed cyclic loading. The results from the proposed damage index are compared with quantitative and qualitative damage indices that have been used in the past to evaluate damage levels in RC structures. The results demonstrate the ability of the method to measure crack distributions and automatically correlate them to a damage index. The method is also applied to an RC shear wall tested under an actual earthquake ground motion record using hybrid simulation to evaluate its performance in a more realistic damage assessment scenario. The results show that the method is able to track the progression of damage to an RC structural element in a realistic earthquake damage scenario. Based on the results, damage grades are proposed that can be used to relate the automatically computed damage index to a specific damage level. The results show great promise for the automatic damage assessment method and is a first step towards automated postdisaster damage assessment of RC structures using digital image correlation.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors gratefully acknowledge the financial support provided by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canada Foundation for Innovative who provided funding for the Carleton Multihazard Research Testing Facility. The authors would also like to acknowledge the National Center for Research on Earthquake Engineering in Taiwan for providing data from the RCCV tests presented in this paper. Image analysis work is financially supported by the Ministry of Science and Technology in Taiwan under project MOST 104-2625-M-027-001.
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Published In
Journal of Structural Engineering
Volume 147 • Issue 4 • April 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Mar 4, 2020
Accepted: Nov 23, 2020
Published online: Jan 23, 2021
Published in print: Apr 1, 2021
Discussion open until: Jun 23, 2021
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