Probabilistic flutter analysis of a long-span bridge in typhoon-prone regions considering climate change and structural deterioration. Issue 215 (August 2021)

• Record Type: Journal Article
• Authors: Chu, Xiaolei; Cui, Wei; Zhao, Lin; Cao, Shuyang; Ge, Yaojun
• Is Part Of: Journal of wind engineering and industrial aerodynamics. Issue 215(2021)
• Publication Date: 2021-08
• Subjects: Long-span bridge -- Probabilistic flutter analysis -- Typhoon hazard -- Climate change -- Structural deterioration -- Generalized density evolution equation; Wind-pressure -- Periodicals; Buildings -- Aerodynamics -- Periodicals; Pression du vent -- Périodiques; Constructions -- Aérodynamique -- Périodiques; Buildings -- Aerodynamics; Wind-pressure; Periodicals
• DOI: 10.1016/j.jweia.2021.104701
• Languages: English
• ISSNs: 0167-6105

Abstract

Abstract: In this paper, a long-term probabilistic flutter analysis of long-span bridges, with consideration of the time-variant probability density function (PDF) of annual maximum wind speed caused by climate change and the deterioration effects of dynamic properties obtained by field monitoring data, is investigated. Estimated uncertainty, which occurs in probabilistic flutter analysis, is quantified by the generalized density evolution equation (GDEE). A suspension bridge, with the center span of 1650 m, is chosen as application example. Long-term deterioration and inter-seasonal varying characteristics of modal frequencies and damping ratios are discussed. An implicit formulation among radius to maximum winds R max, central pressure deficit Δ p, latitude ψ and sea surface temperature SST is set up by training a two-layer feed-forward artificial neural network (ANN) with historical records, and then the full-track typhoon simulation is conducted based on Vickery's empirical model. Lastly, long-term probabilistic flutter analysis is conducted in conjunction with three prospective climate change scenarios, RCP2.6, RCP4.5 and RCP8.5, showing that the likelihood of annual flutter failure will increase greatly mainly due to higher annual maximum wind speed in a warming climate. Highlights: Annual maximum wind speed is estimated under various climate scenarios. Non-stationary distribution parameters of the wind speeds are discussed. A linear regression model is used to predict flutter speed from dynamic properties. Long-term flutter analysis is conducted by generalized density evolution equation.