Stochastic Analysis and Time-Based Modeling of Concrete Bridge Deck Deterioration
Publication: Journal of Bridge Engineering
Volume 23, Issue 9
Abstract
Adequate prediction of the concrete bridge deck deterioration rate is necessary for maintenance and rehabilitation decisions. The stochastic deterioration of bridge decks can be most accurately modeled with a time-based probabilistic approach. In this work, a semi-Markov time-based model, based on accelerated failure time (AFT) Weibull fitted-parameters, was used to estimate the transition probabilities and sojourn times for the deterioration of concrete bridge decks. Approximately 30 years of in-service performance data for over 22,000 bridges in Pennsylvania were used in the model development. The proposed approach attempts to relate deck deterioration rates to various explanatory factors, such as structural system attributes, average daily traffic (ADT), route type, and environmental conditions. The following factors were found to be statistically significant with respect to the rate of bridge deck deterioration: type of rebar protection, continuous versus simply supported spans, overall bridge deck length, number of spans, bridge location, type of overlay, and whether or not the deck was located on interstate routes. Furthermore, the effects of remediation on bridge deck deterioration and service life were also evaluated and quantified, based on in-service performance data.
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Acknowledgments
The authors would also like to acknowledge the financial support from the Pennsylvania DOT (PennDOT). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of PennDOT. The authors would like to extend special thanks to Bob Watral and Marcy Lucas for their technical guidance and to Travis Hoper, who actively participated in all stages of this project.
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Published In
Journal of Bridge Engineering
Volume 23 • Issue 9 • September 2018
Copyright
© 2018 American Society of Civil Engineers.
History
Received: Aug 29, 2017
Accepted: Apr 3, 2018
Published online: Jul 12, 2018
Published in print: Sep 1, 2018
Discussion open until: Dec 12, 2018
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