Prediction of long-term extreme response due to non-Gaussian wind on a HSR cable-stayed bridge by a hybrid approach

The effect of non-Gaussian inflows on structural long-term extreme buffeting responses has been little investi-gated. 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.

Automated summary

This study investigates the sensitivity of long-term extreme value distribution of a high-speed railway cable-stayed bridge to non-Gaussian intensity, with a focus on the torsional angle's reaction to non-Gaussian turbulence wind. The research utilizes a hybrid approach combining machine learning algorithm and virtual process method to study the single and combined effects of turbulence skewness and kurtosis on the bridge's long-term EVDs. The results reveal that the virtual process method is effective for predicting structural long-term EVDs without significant loss of accuracy.