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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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