Displacement estimation for a high-rise building during Super Typhoon Mangkhut based on field measurements and machine learning

Abstract

Knowledge of displacement responses of high-rise buildings under harsh wind excitations is essential for their wind-resistant structural design. This paper develops a machine learning model named long short-term memory (LSTM) to estimate the displacements of a 420-m-high building during Super Typhoon Mangkhut based on available field measurements. The developed model is trained and validated using the field measurements on the building during typhoon events, and the performance of the model is assessed against several evaluation criteria. Then, the trained LSTM model is employed to estimate the displacements of the skyscraper during Mangkhut. The accuracy of the estimated displacements is validated in time and frequency domains. Moreover, the background and resonant components of the estimated displacements during the extreme windstorm are analyzed. This paper aims to provide valuable reference for the wind-resistant design of high-rise buildings in tropical cyclone-prone regions.