Multi-scale fatigue damage prognosis for long-span steel bridges under vehicle loading

Abstract

Fatigue damage prognosis for long-span steel bridges is of the utmost importance in bridge maintenance and management. In this study, a multi-scale fatigue damage prognosis algorithm is developed to calculate the trans-scale fatigue damage accumulation of newly-built long-span steel bridges under vehicle loading. The necessity and procedure of establishing a multi-scale finite element (FE) model of a newly-built long-span bridge for fatigue damage prognosis are first introduced. The future vehicle loading on the bridge is forecasted using the recorded weigh-in-motion (WIM) data and the agent-based traffic flow micro-simulation method. Then, the multi-scale fatigue damage prognosis algorithm is developed based on the multi-scale FE model and using the future vehicle loading. Finally, the proposed algorithm is applied to a newly-built long-span cable-stayed bridge for the time period from 2010 to 2020. The results show that the macro-scale fatigue damage accumulation and micro-scale short crack evolution of the critical components of the bridge can be simultaneously predicted and visualized. The proposed algorithm can be used as a numerical tool for fatigue damage prognosis of steel bridges where (or near where) WIM station is installed.

Keywords:

• Newly-built long-span steel bridges
• vehicle loading
• multi-scale modelling
• fatigue damage prognosis
• micro-scale short cracks

Acknowledgements

The works described in this paper are financially supported by the Hong Kong Research Grants Council (GRF 15218414), the Natural Science Foundation of Jiangsu Province ( BK20170655), and “Zhishan” Young Scholars support program of Southeast University, to which the authors are most grateful. The authors also benefit from useful discussions on traffic flow simulation with Prof. Suren Chen of Colorado State University, USA. Any opinions and conclusions presented in this paper are entirely those of the authors. The authors are also very grateful to all reviewers for their comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work described in this article are financially supported by the Hong Kong Research Grants Council through its competitive grant (RGC 15218414), the Natural Science Foundation of Jiangsu Province (BK20170655) and the National Program on Key Research Project (2016YFC0701301-02), to which the authors are most grateful.