Study of Statistical Uncertainties for Temperature Gradients in Concrete Bridges
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8, Issue 1
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.
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Data Availability Statement
Some data or models that support the findings of this study are available from the corresponding author upon reasonable request, including the finite-element models and field data.
Acknowledgments
This research is sponsored in part by the National Science Foundation, USA (IUSE# 1432397) and the Louisiana Transportation Research Center (Project # 12-1ST) in addition to support from the Department of Civil and Environmental Engineering at Louisiana State University. The support of ThermoAnalytics by providing the license of TAITherm software and for their invaluable guidance is greatly appreciated. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors.
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Information & Authors
Information
Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8 • Issue 1 • March 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Apr 2, 2021
Accepted: Oct 20, 2021
Published online: Dec 8, 2021
Published in print: Mar 1, 2022
Discussion open until: May 8, 2022
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