Big Data for condition evaluation of constructed bridges. (15th June 2017) - Kim, Yail J.; Queiroz, Leticia B.
Highlights: Big Data analytics on the condition evaluation of highway bridges in the United States is provided. The condition rating of the bridge members is examined statistically and probabilistically. The likelihood of deterioration for constructed bridges is predicted. Abstract: This paper presents Big Data analytics on the condition evaluation of highway bridges in the United States. A large dataset comprising 1, 002, 172 bridge decks and superstructures is constructed, based on the National Bridge Inventory (NBI), and categorized into four service zones as specified in the American Association of State Highway Transportation Officials (AASHTO) Load and Resistance Factor Design (LRFD) Bridge Design Specifications. The condition rating of the bridge members is examined statistically and probabilistically, in conjunction with the effect of traffic and environment (i.e., temperature and precipitation). The statistical characterization of the members indicates that concrete-based superstructures are predominant in Zones 1, 2, and 3 (79%, 72%, 85%, respectively), whereas steel- and timber-based superstructures account for 51% and 21% in Zone 4, respectively. The bridges in Zones 1 and 3 are subjected to significantly high traffic-induced loading relative to those in Zones 2 and 4. Thermal loading is noticeable in Zones 1 and 4. The deterioration of bridge decks rapidly develops at the bridges' early service life, and stabilizes with time owing to maintenance and repair efforts. According to a two-factor analysis of variance, adequate selection of structural types dependent upon service environments enhances the performance and longevity of constructed bridges. The likelihood of deterioration of bridges constructed in Zones 1 and 3 is higher than that of the bridges in Zones 2 and 4.