Prediction of Bridge Structural Response Based on Nonstationary Transformer

Abstract

Accurate prediction of bridge structural responses is crucial for infrastructure safety and maintenance. This study introduces the Nonstationary Transformer (NSFormer), a novel model designed to address the challenges posed by nonstationary data in bridge monitoring, characterized by trends, periodicity, and random fluctuations. Unlike traditional models such as LSTM and Transformer, NSFormer leverages a de-stationary attention mechanism that dynamically adapts to changing temporal patterns, enabling robust long-term prediction. Experimental results show that NSFormer consistently outperforms the traditional models across multiple datasets and prediction horizons. Specifically, at a 24-step prediction horizon, NSFormer reduces mean absolute error by at least 22.88% for Deflection dataset and 66.67% for Strain-All dataset. While predictive accuracy decreases with longer horizons, NSFormer maintains superior performance compared to alternatives. Furthermore, prediction accuracy remains stable across varying input horizons, demonstrating the model’s ability to effectively capture temporal dependencies despite data variability. These findings imply that NSFormer can significantly enhance the reliability of structural health monitoring systems by providing more accurate and stable prediction under complex, variable conditions, thereby supporting timely maintenance decisions and improving bridge safety management.