Study of Statistical Uncertainties for Temperature Gradients in Concrete Bridges

Abstract

The philosophy currently adopted by AASHTO Bridge Design Specifications (BDS) is based on considering uncertainties inherent in the structural design process by proposing load and resistance factors. Factors used in the load and resistance factor design (LRFD) method are calibrated using theory of reliability. Research efforts to calibrate load factors for gravity loads can be found in the literature; however, calibration of load factors for temperature loads has not received similar attention. Temperature loads are inevitable environmental loads that affect bridges on a daily and diurnal basis, causing additional stresses and deformations. The first step in conducting such a calibration is to understand the uncertainties inherent therein. This study established a methodology for determining the probability distribution of maximum daily temperature gradient in slab-on-girder concrete bridges using temperature data from the John James Audubon Bridge in south Louisiana. Field data from a finite monitoring time period of 5 years were analyzed statistically and then extrapolated to obtain the largest extreme values over the expected design life of the bridge using extreme value theory. Recorded data were used to investigate the best-fit distribution type for maximum daily temperature differences, which revealed that the beta distribution type is the best fit for maximum daily temperature data. The largest extreme maximum daily temperature difference values were determined using the peak over threshold (POT) method. Extrapolated temperature gradients, that is, the largest extreme maximum daily temperature difference values, were found to be best represented by a Gumbel distribution.