Performance of US Concrete Highway Bridge Decks Characterized by Random Parameters Binary Logistic Regression
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6, Issue 1
Abstract
This study employs a random parameters binary logistic regression (LR) to characterize the impact of environmental and structural parameters on concrete highway bridge deck deterioration nationwide. Two specific gaps in the literature are addressed: (1) the use of a nationwide dataset for analysis, and (2) the implementation of a methodology to account for unobserved heterogeneity. A total of 3,262 bridge deck deterioration observations derived from the authors’ nationwide concrete highway bridge deck performance inventory (NCBDPI) database were used in this study. The deterioration rate (DR) was computed as the decrease in the concrete bridge deck condition rating (CR) over time. Bridge decks with deterioration rates (DR) below a certain threshold were categorized as the lowest deteriorated bridge decks (lowest DR) and decks with DR above a certain threshold were considered among the highest deteriorated (highest DR). The following variables were found to be significant in the final model: average daily truck traffic (ADTT), climatic region, distance from seawater, bridge deck area, age of bridge, type of design and/or construction, structural material design, deck protection, type of membrane, type of wearing surface, and maintenance responsibility. The results show that bridge decks with a high ADTT, age of bridge, bridge decks located in cold regions, and those that are close to seawater are associated with the highest DR group of bridge decks. Furthermore, the type of design and/or construction and maintenance responsibility play a role in decks being associated with highest DR.
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Acknowledgments
The material presented in this article is based upon work supported by the Federal Highway Administration under Cooperative Agreement No. DTFH61-11-H-00027. Any opinions, findings, and conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the Federal Highway Administration. The authors also thank Portland State University and the Oregon Department of Transportation for student and postdoctoral support.
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6 • Issue 1 • March 2020
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©2019 American Society of Civil Engineers.
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Received: Nov 29, 2017
Accepted: May 16, 2019
Published online: Dec 21, 2019
Published in print: Mar 1, 2020
Discussion open until: May 21, 2020
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