TECHNICAL PAPERS
Apr 20, 2011

Rapid Bridge Deck Condition Assessment Using Three-Dimensional Visualization of Impact Echo Data

Publication: Journal of Infrastructure Systems
Volume 18, Issue 1

Abstract

Ultrasonic seismic methods can be used in the condition assessment of bridge decks by evaluating changes in material characteristics and detecting the development of defects and zones of deterioration. The impact-echo method is of special benefit in the evaluation of corrosion-induced deck delamination because of its nondestructive nature and ability to detect delaminated zones at various stages of deterioration: from initial to progressed and developed. The traditional approach in the condition assessment of bridge decks by impact-echo is based on a review of individual test-point records. A new approach based on three-dimensional data visualization is proposed. The developed three-dimensional visualization platform allows for both the advanced presentation and an automated interpretation of impact-echo data. The data presentation is provided in terms of three-dimensional translucent visualizations of reflectors in a bridge deck section and horizontal and vertical cross sections through all distinctive zones, including a zone of delamination. The associated interpretation platform enables the overall assessment of the condition of the deck, through cumulative distributions of reflection intensity of different reflective layers, and the identification of deteriorated zones of the deck to be repaired or rehabilitated in an efficient and intuitive way.

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Published In

Go to Journal of Infrastructure Systems
Journal of Infrastructure Systems
Volume 18Issue 1March 2012
Pages: 12 - 24

History

Received: Jan 19, 2010
Accepted: Apr 19, 2011
Published online: Apr 20, 2011
Published in print: Mar 1, 2012

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Authors

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Nenad Gucunski, A.M.ASCE [email protected]
Professor and Chairman, Dept. of Civil and Environmental Engineering, Rutgers Univ., 623 Bowser Road, Piscataway, NJ 08854 (corresponding author). E-mail: [email protected]
Michelle Yan
Research Engineer, Intelligent Vision and Reasoning Dept., Siemens Corporate Research, 755 College Road East, Princeton, NJ 08540.
Zhe Wang
Research Staff, Thomson Corporate Research, 2 Independence Way, Princeton, NJ 08540.
Tong Fang
Program Manager, Intelligent Vision and Reasoning Dept., Siemens Corporate Research, 755 College Road East, Princeton, NJ 08540.
Ali Maher, M.ASCE
Director, Center for Advanced Infrastructure and Transportation (CAIT), Rutgers Univ., 100 Brett Road, Piscataway, NJ 08854.

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