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Regression Model Evaluation for Highway Bridge Component Deterioration Using National Bridge Inventory Data

Pan Lu, Shiling Pei and Denver Tolliver

Journal of the Transportation Research Forum, 2016, vol. 55, issue 1

Abstract: Accurate prediction of bridge component condition over time is critical for determining a reliable maintenance, repair, and rehabilitation (MRR) strategy for highway bridges. Based on bridge inspection data, regression models are the most-widely adopted tools used by researchers and state agencies to predict future bridge condition (FHWA 2007). Various regression models can produce quite different results because of the differences in modeling assumptions. The evaluation of model quality can be challenging and sometimes subjective. In this study, an external validation procedure was developed to quantitatively compare the forecasting power of different regression models for highway bridge component deterioration. Several regression models for highway bridge component rating over time were compared using the proposed procedure and a traditional apparent model evaluation method based on the goodness-of-fit to data. The results obtained by applying the two methods are compared and discussed in this paper.

Keywords: Public Economics; Research Methods/ Statistical Methods (search for similar items in EconPapers)
Date: 2016
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DOI: 10.22004/ag.econ.262649

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