Identification of bridge section flutter derivatives and numerical calculation of critical flutter wind speed based on deep learning

Artificial Intelligence in Mechanical Manufacturing: From Machine Learning to Generative Pre-trained Transformer

Open Access

Table 2

Neural network model structure and parameter settings.

Layer name	Output size¹	Activation function	notes
Input Layer	(Number of Samples, Time Steps, Features)	None	Input aerodynamic time series data
Convolutional Layer	(Number of Samples, Time Steps, Number of Convolution Kernels)	ReLU²	Extract local features
Pooling Layer	(Number of Samples, Pooled Time Steps, Number of Convolution Kernels)	None	Reduce dimensions and retain important features
Bi-LSTM Layer	(Number of Samples, Time Steps, Number of LSTM Units)	ReLU	Capture long-term dependencies
Fully Connected Layer	(Number of Samples, Output Dimensions)	None	Map to target variables
Output Layer	(Number of Samples, Output Dimensions)	None	Output bridge flutter derivatives

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