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Forecasting Federal Transportation Performance Management Bridge Condition Measures for Bridge Management

Auteur(s):
Médium: article de revue
Langue(s): anglais
Publié dans: Transportation Research Record: Journal of the Transportation Research Board, , n. 10, v. 2675
Page(s): 1287-1296
DOI: 10.1177/03611981211015258
Abstrait:

Many common processes of bridge management can benefit from network-level analysis of long-term costs and condition, on a time frame of about 10 years. Such processes include development and implementation of Transportation Asset Management Plans, long-range needs analysis, capital budgeting and programming, and policy analysis. The ability to forecast federal Transportation Performance Management (TPM) condition measures would provide managers with a way of evaluating the possible outcomes of funding, programming, and policy decisions. A model for this purpose has been developed as a part of StruPlan, an open-source spreadsheet for long-range renewal planning for transportation structures. Element condition state data are found to be highly exponential in distribution, while the federal measures “Percent Good” and “Percent Poor” are categorical when applied to specific bridges. Element data, providing more detail about the type, severity, and extent of defects, are valuable for deterioration modeling, while the TPM measures are simpler for reporting to stakeholders. A set of models was developed to bridge the gap between these measures. Thus far, the models have been calibrated and pilot tested using Idaho, South Dakota, and Kentucky data. The model is a novel approach that has not been attempted elsewhere, that may simplify important parts of bridge management and provide some valuable new ideas for researchers and developers.

Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1177/03611981211015258.
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  • Reference-ID
    10777897
  • Publié(e) le:
    12.05.2024
  • Modifié(e) le:
    12.05.2024
 
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