Deterioration and Predictive Condition Modeling of Concrete Bridge Decks Based on Data from Periodic NDE Surveys
Publication: Journal of Infrastructure Systems
Volume 25, Issue 2
Abstract
A novel approach and program are developed for deterioration and predictive modeling of concrete bridge decks based on nondestructive evaluation (NDE) data. Through an iterative process—combined with data processing, bridge deck segmentation, regression analysis, data integration, and deterioration and predictive modeling—the developed program aids estimates of the remaining service life of bridge decks. Data collected on an actual bridge deck during a period of five and half years are used to illustrate the operation and performance of the developed program. Based on evaluation of condition maps, condition indices, and deterioration curves developed for a range of input parameters, the proposed method quantifies progression of deterioration in bridge deck. By reviewing the predictive models, combined with segmentation of the bridge deck area, a more realistic and practical estimation of the deck’s remaining service life can be made. It is anticipated that the proposed method will provide objective and comprehensive evaluation and prediction of bridge deck condition based on data from multiple NDE technologies.
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Acknowledgments
The authors sincerely acknowledge FHWA support for the Long-Term Bridge Performance (LTBP) Program provided by. The authors are also grateful to the Virginia Department of Transportation for providing access to the bridges in this study. The authors thank the research staff and students at Rutgers’ Center for Advanced Infrastructure and Transportation for their help in data collection. This work was also supported by a National Research Foundation of Korea (NRF) grant (No. NRF-2018R1C1B5031504) and the new faculty research fund of Ajou University.
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Information
Published In
Journal of Infrastructure Systems
Volume 25 • Issue 2 • June 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Jun 8, 2018
Accepted: Oct 22, 2018
Published online: Feb 28, 2019
Published in print: Jun 1, 2019
Discussion open until: Jul 28, 2019
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