Details
| Original language | English |
|---|---|
| Article number | 062002 |
| Number of pages | 11 |
| Journal | Journal of Physics: Conference Series |
| Volume | 2647 |
| Issue number | 6 |
| Early online date | 28 Jun 2024 |
| Publication status | Published - 2024 |
| Event | 12th International Conference on Structural Dynamics, EURODYN 2023 - Delft, Netherlands Duration: 2 Jul 2023 → 5 Jul 2023 |
Abstract
This paper proposes a framework for efficient time-dependent reliability analysis for a parametrized stochastic dynamic system, namely a train bridge load model with uncertain design properties. The Probability Density Evolution Method is utilized to explore the multidimensional random space, identify specific failure paths contributing to the failure region, and provide a full probabilistic output of the desired target quantity. The framework is tested on an uncertain railway bridge subjected to train transit (moving loads). The peak acceleration as a function of the train speed in a certain interval is analysed and utilised as performance criteria. The main sources of uncertainties are the damping and the bridge's moment of inertia. The full evolutionary Probability Density Function of the bridge's maximum deck acceleration is obtained, the reliability is assessed and a probability of failure estimated. The results show that in the considered speed intervals, the velocities contributing to the failure region are depending on the underlying sampling method. The Probability Density Evolution Method offers additional insight on the evolution of the critical peak accelerations while at the same time performing a reasonable amount of full model evaluations. The study concludes that further discussion is needed to determine the appropriate prediction of the train speeds that may or may not significantly contribute to the probability of failure in this bridge train model.
ASJC Scopus subject areas
- Physics and Astronomy(all)
- General Physics and Astronomy
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In: Journal of Physics: Conference Series, Vol. 2647, No. 6, 062002, 2024.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Efficient time-dependent reliability analysis for a railway bridge model
AU - Bittner, M.
AU - Fritsch, L.
AU - Hirzinger, B.
AU - Broggi, M.
AU - Beer, M.
N1 - Publisher Copyright: © Published under licence by IOP Publishing Ltd.
PY - 2024
Y1 - 2024
N2 - This paper proposes a framework for efficient time-dependent reliability analysis for a parametrized stochastic dynamic system, namely a train bridge load model with uncertain design properties. The Probability Density Evolution Method is utilized to explore the multidimensional random space, identify specific failure paths contributing to the failure region, and provide a full probabilistic output of the desired target quantity. The framework is tested on an uncertain railway bridge subjected to train transit (moving loads). The peak acceleration as a function of the train speed in a certain interval is analysed and utilised as performance criteria. The main sources of uncertainties are the damping and the bridge's moment of inertia. The full evolutionary Probability Density Function of the bridge's maximum deck acceleration is obtained, the reliability is assessed and a probability of failure estimated. The results show that in the considered speed intervals, the velocities contributing to the failure region are depending on the underlying sampling method. The Probability Density Evolution Method offers additional insight on the evolution of the critical peak accelerations while at the same time performing a reasonable amount of full model evaluations. The study concludes that further discussion is needed to determine the appropriate prediction of the train speeds that may or may not significantly contribute to the probability of failure in this bridge train model.
AB - This paper proposes a framework for efficient time-dependent reliability analysis for a parametrized stochastic dynamic system, namely a train bridge load model with uncertain design properties. The Probability Density Evolution Method is utilized to explore the multidimensional random space, identify specific failure paths contributing to the failure region, and provide a full probabilistic output of the desired target quantity. The framework is tested on an uncertain railway bridge subjected to train transit (moving loads). The peak acceleration as a function of the train speed in a certain interval is analysed and utilised as performance criteria. The main sources of uncertainties are the damping and the bridge's moment of inertia. The full evolutionary Probability Density Function of the bridge's maximum deck acceleration is obtained, the reliability is assessed and a probability of failure estimated. The results show that in the considered speed intervals, the velocities contributing to the failure region are depending on the underlying sampling method. The Probability Density Evolution Method offers additional insight on the evolution of the critical peak accelerations while at the same time performing a reasonable amount of full model evaluations. The study concludes that further discussion is needed to determine the appropriate prediction of the train speeds that may or may not significantly contribute to the probability of failure in this bridge train model.
UR - http://www.scopus.com/inward/record.url?scp=85198029877&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/2647/6/062002
DO - 10.1088/1742-6596/2647/6/062002
M3 - Conference article
AN - SCOPUS:85198029877
VL - 2647
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 6
M1 - 062002
T2 - 12th International Conference on Structural Dynamics, EURODYN 2023
Y2 - 2 July 2023 through 5 July 2023
ER -