Guidelines for Prioritization of Bridge Replacement, Rehabilitation, and Preservation Projects
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2019-07-30
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Edition:Final report, August 1, 2015 – July 31, 2018
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Abstract:Identification and prioritization of bridge replacement projects is one of the most critical aspects of an effective Bridge Management System. Currently, North Carolina Department of Transportation (NCDOT) uses a Priority Replacement Index (PRI) that has developed as an ad-hoc ranking scale that lacks transparency between performance measures and priority and does not consistently reflect the preferences and priorities of NCDOT personnel that select bridge replacement projects. The objective of this research is revise the performance criteria and measures that are considered by the index and establish a transparent and objective relative weighting of measures for identification and prioritization of bridge replacement projects. Two practitioner surveys completed by Division Bridge Program Managers, Bridge Maintenance Engineers, and members of the Steering and Implementation Committee are used to address these objectives. Prioritization models derived from the survey responses as well as data-driven prioritization models developed through statistical regression are evaluated against asset management databases. The research identifies two alternatives to the PRI that improve the transparency of the index and incorporate a more comprehensive set of performance criteria and measures. One index is developed through binary logistic regression and provides a forecasted probability that a bridge will be selected for replacement. This binary logistic regression model produces a better distribution of scores for the bridges in the state inventory and results in fewer instances of bridges not scheduled for replacement receiving high priority for replacement. The second index is developed as a simple weighted average of all performance measures identified by the practitioner surveys as significant to the prioritization of bridge replacement projects. While the classification performance and distribution of scores is not as desirable as that produced by the binary logistic regression model, this index produces the most transparent link between performance measures and the priority score, with relative weighting of individual performance criteria that is reasonably consistent with the preference structure elicited from the practitioner surveys.
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