Wind field characterization with skip-connected variational autoencoder for data cleaning under deck disturbance effects

Abstract

Accurate characterization of wind fields is essential for effectively analyzing wind-induced responses of long-span bridges. However, field measurements often suffer from severe anomalies, compromising their reliability. One significant cause of these anomalies is the disturbances induced by the bridge deck. This study aims to eliminate the effects of such disturbances on the measured data and wind field characteristics. We begin with a comparative analysis of disturbed versus undisturbed wind measurements. To remove the disturbance effects and clean the data, we introduce a novel skip-connected variational autoencoder (SVAE) neural network that can effectively reconstruct undisturbed data from disturbed measurements. The SVAE is trained on a dataset comprising both disturbed and undisturbed measurements collected during strong wind events and is validated against other generative models based on reconstruction accuracy. We then employ the trained SVAE to clean measurements from another event and analyze wind field characteristics such as mean wind speed, turbulence intensity, integral scale of turbulence, and power spectral density from the reconstructed data. Our findings confirm the effectiveness of this approach, suggesting its broader applicability in ensuring the accuracy and reliability of wind field characterizations and subsequent analyses of wind-induced responses on long-span bridges.