Abstract: Aiming at the various types of time series data collected by the current bridge structural health monitoring system， in view of the bridge structural response and the additional impact of the environment on the data， in order to achieve bridge structural safety early warning， based on the principle of integrated algorithm， this paper adopts ICEEMDAN （Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise）method studied by the improved current empirical modal decomposition method to decompose the bridge monitoring stress data， and uses the multi-scale permutation entropy algorithm to sort and reorganize the decomposed components. Finally， we combine the classical time series analysis theory BiLSTM (Bidirectional Long Short-Term Memory) network with differential ARIMA (Autoregressive Integrated Moving Average Model) to make predictive analysis for the reconstituted component and combine the results to get the final predicted value. By verifying the stress data collected by the health monitoring system of the Dadu River Bridge on the Yakang Expressway， the results show that this method effectively improves the prediction effect compared with the single model， with an overall increase of about 60%~70%. The method achieves accurate prediction of bridge monitoring data and lays a strong foundation for future bridge structure health state prediction， digital construction and safety early warning.

Key words: bridge, stress, health monitoring, ICEEMDAN, combined prediction model