Physics‐Informed Transfer Learning for the Identification of Fluctuating Wind Velocity Spectra of High‐Rise Buildings Using Partial Structural Responses

The identification of fluctuating wind velocity spectra of high‐rise buildings using partial structural responses is challenging due to the ill‐posed nature of the inverse problem. In this paper, two frequency‐domain physics‐informed deep learning methods are proposed to identify stationary fluctuating wind velocity spectra of high‐rise buildings using partial structural responses. The first method targets uniform wind spectra by exploiting their spatial correlations with a one‐dimensional discrete cosine transform (DCT). Structural partial response spectra are used as the inputs to a physics‐informed convolutional neural network (CNN), and the DCT coefficient vector of the wind spectra serves as the outputs, and this physical relationship is embedded into the physical loss function of the CNN. Furthermore, transfer learning is proposed so that the pretrained parameters for a specific frequency of the physics‐informed CNN are used as the initial values of the network parameters at other frequencies, facilitating the identification of wind spectra across different frequencies. Then, it is extended to study the more complex identification of nonuniform wind velocity spectra of high‐rise buildings, and a physics‐informed transfer learning identification method is proposed based on the two‐dimensional DCT of nonuniform wind spectra. Analogously, structural partial response spectra and the two‐dimensional DCT coefficients are used as the CNN inputs and outputs, respectively. The effectiveness of the proposed methods is validated by identifying the uniform and nonuniform fluctuating wind velocity spectra of the wind‐excited 76‐story benchmark building, respectively.