American Society of Civil Engineers


Statistical Distribution of Bridge Resistance Using Updated Material Parameters


by Sarah L. Orton, Ph.D., (corresponding author), M.ASCE, (Assistant Professor, Univ. of Missouri Columbia, E2503 Lafferre Hall, Columbia, MO 65211. E-mail: ortons@missouri.edu), Oh-Sung Kwon, Ph.D., M.ASCE, (Assistant Professor, Univ. of Toronto, 242A 35 St. George St, Toronto, ON M5S 1A4. E-mail: kwon@utoronto.ca), and Timothy Hazlett, (Graduate Research Assistant, Univ. of Missouri Columbia, E2509 Lafferre Hall, Columbia, MO 65211. E-mail: tahtgb@mizzou.edu)

Journal of Bridge Engineering, Vol. 17, No. 3, May/June 2012, pp. 462-469, (doi:  http://dx.doi.org/10.1061/(ASCE)BE.1943-5592.0000278)

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Document type: Journal Paper
Abstract: Resistance (load-carrying capacity) of a bridge girder is a random variable and can be determined by considering the uncertainty in material, fabrication, and professional/analysis properties. Previous calibrations of load and resistance factor design (LRFD) determined the distribution of bridge resistance on the basis of data from more than 30 years ago. This study uses the latest Material properties available in, the literature to update the resistance distribution. The statistical distribution of the resistance was determined through Monte Carlo simulation. The results of the analysis show an increase in bias and a decrease in the coefficient of variation (COV) for all types of bridges in comparison with those used in previous calibration studies. The changes in bias and COV are the result of higher bias and lower COV in material properties owing to better quality control in concrete and steel manufacturing. Steel and concrete bridges saw the greatest change in resistance distribution. Prestressed bridges saw little change because the material properties of prestressing steel, which is the most sensitive parameter in the prestressed bridges, did not change significantly since the previous calibration study. With these resistance distributions, it is expected that the calibration of the load factor in the AASHTO specification will lead to a lower live load factor, thereby possibly reducing the material cost of the bridge. In addition, the ratio of actual to required (design) resistances of representative bridges in Missouri was determined. The analysis showed that almost all representative bridges had a capacity-to-demand ratio greater than 1 according to current AASHTO standards.


ASCE Subject Headings:
Bridges
Prestressed concrete
Reinforced concrete
Steel
Reliability
Load and resistance factor design
Material properties
Statistics

Author Keywords:
Bridges
Prestressed concrete
Reinforced concrete
Steel
Reliability
LRFD
Material properties