Sponsor
This material is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange under DTFH61-11-H-00027. The U.S. Government assumes no liability for the use of the information. The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers’ names appear in this material only because they are considered essential to the objective of the material. They are included for informational purposes only and are not intended to reflect a preference, approval, or endorsement of any one product or entity.
Document Type
Report
Publication Date
9-2018
Subjects
Concrete bridges -- Floors -- Deterioration, Bridges -- Floors -- Deterioration, Concrete bridges -- Design and construction -- Statistical analysis, Concrete bridges -- United States -- Maintenance and repair -- Testing
Abstract
Concrete highway bridge deck repairs represent the highest expense associated with bridge maintenance cost. In order to optimize such activities and use the available monies effectively, a solid understanding of the parameters that affect the performance of concrete bridge decks is critical. The National Bridge Inventory (NBI), perhaps the single-most comprehensive source of bridge information, gathers data on more than 600,000 bridges in all fifty states, the District of Columbia, and the Commonwealth of Puerto Rico. Focusing on concrete highway bridge deck performance, this research developed a nationwide database based on NBI data and other critical parameters that were computed by the authors, referred to as the Nationwide Concrete Highway Bridge Deck Performance Inventory (NCBDPI) database. Additionally, two performance parameters were computed from the available concrete bridge deck condition ratings (CR): Time-in-condition rating (TICR) and deterioration rate (DR). Following the aggregation of all these parameters in the NCBDPI database, filtering, and processing were performed. In addition to a basic prescriptive analysis, two types of advanced analysis were applied to the new dataset. First, binary logistic regression was applied to a subset of the data consisting of the highest and lowest DR. Second, a Bayesian survival analysis was performed on the TICR considering censored data. Through the analyses it was possible to show which parameters influence deck performance and create tools that can help agencies and bridge owners make better decisions regarding concrete bridge deck preservation.
Persistent Identifier
https://archives.pdx.edu/ds/psu/26267
Citation Details
Ghonima, Omar; Schumacher, Thomas; Unnikrishnan, Avinash; and Fleischhacker, Adam, "Advancing Bridge Technology, Task 10: Statistical Analysis and Modeling of US Concrete Highway Bridge Deck Performance -- Internal Final Report" (2018). Civil and Environmental Engineering Faculty Publications and Presentations. 443.
https://archives.pdx.edu/ds/psu/26267
Included in
Civil Engineering Commons, Construction Engineering and Management Commons, Structural Engineering Commons
Description
At head of title: FHWA Collaborative Project.
Chapter 4 has been published in revised form under: Ghonima, O., Anderson, J. C., Schumacher, T., and Unnikrishnan, A. (2020). Performance of US Concrete Highway Bridge Decks Characterized by Random Parameters Binary Logistic Regression. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. Vol. 6(1). DOI: https://doi.org/10.1061/AJRUA6.0001031.
Chapter 5 has been published in revised form under: Fleischhacker, A., Ghonima, O., and Schumacher, T. (2020). Bayesian Survival Analysis for US Concrete Highway Bridge Decks. ASCE Journal of Infrastructure Systems. Vol. 26(1). DOI: https://doi.org/10.1061/(ASCE)IS.1943-555X.0000511.