Time transformer for enhanced bridge deflection prediction with CT-MSA and KAN

Bridge deflection is a crucial parameter in diagnosing bridge structural health, and accurately predicting deflection is vital for ensuring bridges operate safely and effectively. Traditional deflection timing prediction methods may be affected by future information. To address this issue, this paper proposes an improved timing prediction Transformer (TSTF) model, which introduces causal time multi-head self-attention mechanism (CT-MSA) to ensure that the model only relies on past information when forecasting. The multi-scale time dependence is extracted by multi-layer window size setting method. This paper also introduces KAN (Key-Attention-Neural) full connection layer in TSFT model to further improve the accuracy and stability of deflection prediction. Data sensitivity analysis is also performed to test the influence of input data at different locations on model predictions. In the experiment, the long-term historical deflection data of the bridge is preprocessed. By comparing TSFT model with traditional time series forecasting methods, the TSFT model based on CT-MSA and KAN fully connected layer has better performance in forecasting accuracy and computing efficiency.