Random Field-Based Time-Dependent Reliability Analyses of a PSC Box-Girder Bridge
Source abstract
Abstract
The parameters affecting structural reliability are usually regarded as independent random variables in the current reliability analyses of bridge structures while the randomness of these structural parameters in spatial distribution is neglected. To overcome this disadvantage, the random field model should be used to describe their probability distribution. In this paper, a structural reliability analysis method considering the effect of a random field is proposed, and its validity is verified by three numerical case studies. A prestressed concrete (PSC) box-girder bridge with a main span of 150 m is selected for demonstration, and the importance sampling (IS) method is applied to estimate its failure probability, in which the influences of shrinkage and creep, stress relaxation, and shear lag on the time-dependent performance of the structure are taken into account. In addition, the random fields of the structure are discretized by using the local average method (LAM). Finally, the effects of random field parameters, such as the number of discrete elements, correlation model, and correlation length, on the reliability of the box-girder bridge are discussed.
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Research context
OpenAlex topics: Concrete Corrosion and Durability · Concrete Properties and Behavior · Structural Behavior of Reinforced Concrete
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
Key Laboratory of Concrete and Prestressed Concrete Structures, Ministry of Education, Southeast University, Nanjing 210096, China
School of Architecture and Civil Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Department of Civil Engineering, Aalto University, Rakentajanaukio 4 A, 02150 Espoo, Finland
Funding
National Natural Science Foundation of China · Natural Science Foundation of Jiangsu Province
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