Probabilistic Fatigue Life Evaluation of Steel Railway Bridges according to Live-Dead Loads Ratio

강철도교의 활하중-사하중 비에 따른 확률기반 피로수명 평가

  • Lee, Sangmok (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) ;
  • Lee, Young-Joo (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
  • 이상목 (울산과학기술원 도시환경공학부) ;
  • 이영주 (울산과학기술원 도시환경공학부)
  • Received : 2018.11.11
  • Accepted : 2019.01.04
  • Published : 2019.01.31


Various studies have been conducted to evaluate the probabilistic fatigue life of steel railway bridges, but many of them are based on a relatively simple model of crack propagation. The model assumes zero minimum stress and constant loading amplitude, which is not appropriate for the fatigue life evaluation of railway bridges. Thus, this study proposes a new probabilistic method employing an advanced crack propagation model that considers the live-dead load ratio for the fatigue life evaluation of steel railway bridges. In addition, by using the rainflow cycle counting algorithm, it can handle variable-amplitude loading, which is the most common loading pattern for railway bridges. To demonstrate the proposed method, it was applied to a numerical example of a steel railway bridge, and the fatigue lives of the major components and structural system were estimated. Furthermore, the effects of various ratios of live-dead loads on bridge fatigue life were examined through a parametric study. As a result, with the increasing live-dead stress ratio from 0 to 5/6, the fatigue lives can be reduced by approximately 30 years at both the component and system levels.

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Fig. 1. Cyclic loading for various values of R

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Fig. 2. Steel railway bridge example

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Fig. 3. Stress history for Members 13 and 27

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Fig. 4. Reliability indices for five selected members obtained via proposed method (R=3/4)

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Fig. 5. Reliability indices for two selected members and the entire system, as derived via the proposed method

Table 1. Maximum stress values of the five structural members with the overall highest stress values

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Table 2. Statistical properties of random variables (RVs)

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Table 3. Statistical properties of random variables

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Supported by : 국토교통과학기술진흥원


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