Deterioration modeling of highway bridge components using deterministic and stochastic methods
Proper timing of bridge repair or reconstruction is critical for the bridge management functions including long-term planning and budgeting, and hinges heavily upon the reliability of bridge deterioration prediction. Bridge deterioration models can be used not only for making such predictions but also to identify the factors that affect bridge component deterioration, and to measure the strength of these factors. The current literature lacks adequate and comprehensive assessment of bridge component deterioration using recent data that accounts for a wide range of potential explanatory factors including service type, design type, and climate. In addressing this lacuna in the literature, this thesis carefully assembled a comprehensive bridge dataset using data from the National Bridge Inventory and the National Oceanic and Atmospheric Administration, developed a technique for data quality/integrity enhancement, and estimated statistical models for bridge components for each of several bridge families. The bridge families were created based on their functional classification and rehabilitation history. For each component (deck, superstructure and substructure) within each family, deterministic and stochastic models were developed. The deterministic models used ordinary least squares estimation, while the stochastic models used an ordered probit specification. The models also threw more light on the direction and strengths of influence of the explanatory factors of bridge component deterioration, including service type, design type, material type, and climate. Age was consistently found to be the most influential factor. The study results are useful for the various tasks associated with bridge management including maintenance and rehabilitation programming and budgeting, cost allocation and bridge asset valuation.
Labi, Purdue University.
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