SAR-Nbeats-based temperature interpretation and prediction of InSAR time-series deformations for bridges

Abstract

Existing bridge monitoring methods face high monitoring costs, and the processing and forecasting of monitoring data often rely on machine learning which lacks interpretability in the prediction results. Based on the Neural Basis Expansion Analysis for Time Series Forecasting (N-Beats) model, this study proposes an SAR-Nbeats (S-N) model for extracting, decomposition, and predicting bridge deformation. The input of S-N model is the bridge deformation data, which are obtained by Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique. At first, using Sentinel-1A imagery from 2018 to 2023 as the data source, bridge deformation results are obtained through PS-InSAR technology. Then, extremum symmetric mode decomposition and seasonal-trend decomposition are employed to decompose bridge deformation into trend, seasonal, and random components effectively, conducting adaptive classification based on different periods. Finally, based on the periodic and trend characteristics of bridge deformation, improvements and parameter adjustments are made to conventional N-Beats algorithms. The time-series data after decomposed are used as input to train the improved N-Beats model and obtain prediction results. Compared with the original algorithm, the main improvements include transforming the input data into modal decomposed data and associating the parameters of the fitting function with the deformation composition of the bridge. The bridge deformation patterns were evaluated based on climatic rules and InSAR time-series prediction results, yielding the following findings: by comparing the prediction results, the performance of SAR-Nbeats model is better than Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average Model (ARIMA), which has highest R² 0.8605. The SAR-Nbeats model improves the accuracy and interpretability of bridge deformation predictions by refining the input of the data-driven forecasting model. It can achieve the goal of monitoring and early warning for bridges without ground-based monitoring systems with lower computational effort and faster processing speeds.