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Bridge deterioration models to support Indiana’s bridge management system.

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  • Abstract:
    An effective bridge management system that is equipped with reliable deterioration models enables agency engineers to carry out ; monitoring and long-term programming of bridge repair actions. At the project level, deterioration models help the agency to track the ; physical condition of bridge elements and to specify when bridge maintenance, rehabilitation and replacement can be expected. Also, ; with reliable deterioration models, the agency can customize bridge repair or replacement schedules that incorporate element ; condition, functional obsolescence, and pre-specified performance thresholds. At the network level, component-specific deterioration ; models are useful for system-wide needs assessment over a specified future time horizon, and to quantifying the system-wide ; consequences of funding shortfalls or funding increases in terms of specified performance measures including average values of bridge ; condition and remaining service life. ; The bridge deterioration models that are currently in use in the Indiana Bridge Management System were developed over two decades ; ago. Since then, significant changes have taken place in inspection methods, technologies used, advanced statistical tools for data ; analysis. Also, because of the lack of reliable data, such items as the truck traffic and climate conditions were not included in past ; modeling efforts. In recent years, these obstacles have been minimized and therefore, there is an opportunity to update the ; deterioration models for the various bridge components. ; In addressing this research need, the present study developed families of curves representing deterioration models for bridge deck, ; superstructure, and the substructure. The National Bridge Inventory database was used, and the models use the NBI condition ratings as ; the response variable. The model families were categorized by administrative region, functional class, and superstructure material type. ; The explanatory variables include traffic volume and truck traffic, design type, and climatic condition, and design features. Deterministic ; and probabilistic models were developed.
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    urn:sha-512:c0fb84fdff6e4a58470f8e9f679859893e9eb215e7f592a705a6f520e984115c707cbb6b9867180e132bcfa28e5aab10764c6c4f0bf8f0feb0ec3a56437f09a9
File Language:
English
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