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Stochastic modelling and optimum inspection and maintenance strategy for fatigue affected steel bridge members

  • Huang, Tian-Li (School of Civil Engineering, Central South University) ;
  • Zhou, Hao (School of Civil Engineering, Central South University) ;
  • Chen, Hua-Peng (School of Civil Engineering, Central South University) ;
  • Ren, Wei-Xin (School of Civil Engineering, Central South University)
  • Received : 2015.09.14
  • Accepted : 2016.03.20
  • Published : 2016.09.25
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Abstract

This paper presents a method for stochastic modelling of fatigue crack growth and optimising inspection and maintenance strategy for the structural members of steel bridges. The fatigue crack evolution is considered as a stochastic process with uncertainties, and the Gamma process is adopted to simulate the propagation of fatigue crack in steel bridge members. From the stochastic modelling for fatigue crack growth, the probability of failure caused by fatigue is predicted over the service life of steel bridge members. The remaining fatigue life of steel bridge members is determined by comparing the fatigue crack length with its predetermined threshold. Furthermore, the probability of detection is adopted to consider the uncertainties in detecting fatigue crack by using existing damage detection techniques. A multi-objective optimisation problem is proposed and solved by a genetic algorithm to determine the optimised inspection and maintenance strategy for the fatigue affected steel bridge members. The optimised strategy is achieved by minimizing the life-cycle cost, including the inspection, maintenance and failure costs, and maximizing the service life after necessary intervention. The number of intervention during the service life is also taken into account to investigate the relationship between the service life and the cost for maintenance. The results from numerical examples show that the proposed method can provide a useful approach for cost-effective inspection and maintenance strategy for fatigue affected steel bridges.

Keywords

Acknowledgement

Supported by : Natural Science Foundation of China

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