Reconstruction of missing wind data based on limited wind pressure measurements and machine learning

In structural health monitoring (SHM), wind field monitoring sometimes suffers from data loss owing to monitoring device failure, which inevitably creates barriers to subsequent data analysis and data mining. To this end, a novel strategy for reconstructing missing wind field data based on machine learning (ML) utilizing limited wind pressure measurements is proposed in this paper. Several ML algorithms, including decision tree, random forest, gradient boosting regression tree, support vector regression, Gaussian process regression, and backpropagation neural network, are employed to characterize potential relationships between wind pressure information (including time series and statistical parameters of wind pressures) and wind field information (e.g., wind direction and wind speed). Moreover, the effect of input information (including the type of input variables as well as the number and location of pressure transducers providing input data) on reconstruction performance and efficiency is investigated. Field measured records from an SHM system in a 600-m-high supertall building during typhoons are utilized to validate the feasibility and robustness of the proposed strategy. The results show that the presented strategy can effectively reconstruct missing wind field information in the SHM of the skyscraper during typhoons. Compared with the time series of wind pressures, selecting statistical parameters of wind pressures as input variables can effectively improve the performance and efficiency of reconstruction models. Choosing appropriate input information (e.g., using multiple input variables, adopting data from a larger number of pressure transducers, and utilizing data from pressure transducers closer to an anemometer) is beneficial for enhancing the performance of reconstruction models.