Using Classification Trees for Predicting National Bridge Inventory Condition Ratings

Abstract

In bridge management practice, bridge condition is the fundamental information needed to allow decision makers to make well-informed decisions regarding preservation, rehabilitation, or replacement of a bridge or network of bridges. The National Bridge Inventory (NBI) condition ratings, collected since the early 1970s, and the Commonly Recognized (CoRe) element condition data, collected since the early 1990s, are two major sources of bridge condition data in the United States. General NBI condition ratings are utilized for performance assessment, performance reporting, resource allocation, and selection of bridge projects by all levels of government. Since the early 1990s, the bridge management community has been interested in an algorithm to predict the categorical NBI condition rating classes from the more quantitative and detailed CoRe element condition data. An algorithm with sufficient predictive accuracy would make only CoRe element inspections necessary and would provide time and resource savings. This paper presents a new methodology for this purpose, using classification and regression trees (CARTs). The CART analyses were conducted with the bridge condition data provided by three state transportation agencies, using data from 2006 to 2010. The statistical results point to a more accurate prediction method than the previous algorithms described in the literature.