Stochastic analysis of fatigue of concrete bridges

Abstract

A novel method is proposed in this work for the assessment of the remaining fatigue life and fatigue reliability of concrete bridges subjected to random loads. The fatigue reliability of a bridge is a function of the fatigue damage accumulation; a stochastic fatigue damage model (SFDM) with physical mechanism is introduced for deriving the fatigue damage process. In order to implement the probabilistic analysis, based on the probability density evolution method (PDEM), the generalised density evolution equation (GDEE) for the remaining fatigue life is developed. Finally, a prestressed concrete continuous beam bridge located in China is illustrated. The random fatigue load acting on the bridge is modelled as the compound Poisson process, and the simulation of the random load uses the stochastic harmonic function (SHF) method. To simplify the reliability analysis, an equivalent constant-amplitude (ECA) load process is introduced based on energy equivalence. By employing SFDM, the finite element analysis of the bridge under the fatigue loading is performed. Then, the fatigue damage accumulation process of the bridge under the fatigue loading is obtained. Through solving the probability density evolution equation for the remaining fatigue life, the probability density functions (PDFs) of the remaining fatigue life evolving with time is obtained. The fatigue reliability is then calculated by integrating the PDF of the corresponding remaining life.

Keywords:

• Fatigue reliability
• concrete bridges
• stochastic fatigue damage model
• probability density evolution method

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was performed at the University of California, Irvine during the one-year that Ms Ruofan Gao spent as Visiting Scholar performing her doctoral dissertation for her Ph.D. degree at the Tongji University in Shanghai, China. Her research was supported by the Chinese Scholarship Committee. This work was supported by National Natural Science Foundation of China [grant number 51538010], [grant number 51261120374].