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- W4308149658 abstract "The effect of non-Gaussian inflows on structural long-term extreme buffeting responses has been little investigated. In this study, the sensitivity of long-term extreme value distribution (EVD) of a high-speed railway cable-stayed bridge to the non-Gaussian intensity is studied first. The turbulence skewness and kurtosis are then taken as the environmental variables to investigate their single and combined effects on bridge's long-term EVDs based on a proposed hybrid approach that combines the machine learning algorithm and virtual process method. The 2.5-year measured turbulence wind and 40-year annual extreme wind speed recorded near the bridge site are utilized to describe the probability distributions of the skewness and kurtosis of turbulence wind and 10-min mean wind speed. The research results reveal that: (1) the long-term EVD of torsional angle is more sensitive to non-Gaussian turbulence wind than vertical and lateral extreme responses; (2) the single effect of turbulence skewness is detrimental but limited, and the combined effect of skewness and kurtosis of turbulence u ( w ) is also weak within the considered MRIs (1–100 years). Lastly, the virtual process method is shown to be applicable to predict structural long-term EVDs; and it is efficient without losing significant prediction accuracy. • A hybrid approach is proposed to improve the analysis efficiency of structure long-term extreme response. • The long-term extreme response of a cable-stayed bridge under non-Gaussian inflow is investigated comprehensively. • The applicability and efficiency of virtual process method in evaluating structural long-term EVD are validated. • The probability models of turbulence high-order statics are constructed based on long-term monitoring wind data." @default.
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- W4308149658 date "2022-12-01" @default.
- W4308149658 modified "2023-09-26" @default.
- W4308149658 title "Prediction of long-term extreme response due to non-Gaussian wind on a HSR cable-stayed bridge by a hybrid approach" @default.
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- W4308149658 doi "https://doi.org/10.1016/j.jweia.2022.105217" @default.
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