Physics guided wavelet convolutional neural network for wind-induced vibration modeling with application to structural dynamic reliability analysis

Deep neural network (NN) has become one of the common choices of surrogate model for reliability analysis of structural dynamic response under complex wind loads. However, the cost of data acquisition is usually prohibitive, making it hard for NN training to accurately replace the complex wind-structure coupled system using the limited data. In this work, a physics guided wavelet convolutional NN (PGwCNN) is regarded as surrogate model of a wind turbine tower (WinTT) and utilized for wind effects prediction. The law of dynamics is regarded as the prior knowledge and encoded in the NN, which can enhance the learning capability of the trained model. Then, the performance of the PGwCNN is demonstrated through the dynamic reliability analysis of a high and flexible WinTT using probability density evolution method (PDEM). What's more, the L2 regularization and dropout layers are utilized to alleviate the overfitting issues. N-fold cross validation and grid search method are adopted to maximize the use of the limited training datasets. Results demonstrate that PGwCNN is capable to reach a compromise between efficiency and accuracy for predicting wind effects on the WinTT. In addition, the PGwCNN can effectively enhance the calculation efficiency of the reliability analysis.