Prediction of long-term extreme response due to non-Gaussian wind on a HSR cable-stayed bridge by a hybrid approach. Issue 231 (December 2022)

• Record Type: Journal Article
• Authors: Xu, Zhiwei; Dai, Gonglian; Chen, Y Frank; Rao, Huiming
• Is Part Of: Journal of wind engineering and industrial aerodynamics. Issue 231(2022)
• Publication Date: 2022-12
• Subjects: Bridge engineering -- Extreme response -- Non-Gaussian wind -- Machine learning -- Wind field measurement -- High-speed railway; Wind-pressure -- Periodicals; Buildings -- Aerodynamics -- Periodicals; Pression du vent -- Périodiques; Constructions -- Aérodynamique -- Périodiques; Buildings -- Aerodynamics; Wind-pressure; Periodicals
• DOI: 10.1016/j.jweia.2022.105217
• Languages: English
• ISSNs: 0167-6105

Abstract

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. Highlights: 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.