# A dynamic early-warning method for bridge structural safety based on data reconstruction and depth prediction

## TL;DR

This paper introduces a new method to monitor bridge safety by using data reconstruction and deep learning to detect abnormal structural behavior in real time.

## Contribution

A novel dynamic early-warning system for bridge safety using data reconstruction and LSTM-based prediction to detect anomalies.

## Key findings

- SVD-based data reconstruction effectively decouples complex monitoring data.
- LSTM networks improve prediction accuracy of bridge structural responses.
- The method enables real-time anomaly detection using adaptive thresholds.

## Abstract

The structural response of bridges involves a complex interplay of various coupled effects, rendering the identification of long-term variation trends inherently challenging. Consequently, effectively detecting and alerting abnormal monitoring data for bridge structures under complex coupled loads remains a significant difficulty. To address this issue, this study proposes a dynamic early-warning method for bridge structural safety, leveraging data reconstruction and deep learning-based prediction. First, the singular value decomposition (SVD) algorithm is employed to decompose and reconstruct the monitoring data based on the contribution rate of influencing factors, thereby decoupling the data from various coupled effects. Second, a deep learning architecture utilizing a long short-term memory (LSTM) network is applied to establish a prediction model for each group of decomposed monitoring data, significantly enhancing prediction accuracy. Building on this foundation, the dynamic early-warning system for bridge structural safety is realized by integrating anomaly diagnosis theory with both predicted and measured data. A validation case using measured strain data demonstrates that the proposed method accurately predicts bridge strain data and calculates real-time adaptive thresholds, enabling real-time detection of anomalous monitoring data.