Size of FEMs of bridges.

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.桥梁结构响应涉及多种耦合效应的复杂相互作用，使得长期变化趋势的识别本身就极具挑战性。因此，在复杂耦合荷载作用下，有效检测并预警桥梁结构的异常监测数据仍是一项重大难题。为解决这一问题，本研究提出了一种基于数据重构与深度学习预测的桥梁结构安全动态预警方法。首先，采用奇异值分解（Singular Value Decomposition, SVD）算法，基于影响因素的贡献率对监测数据进行分解与重构，从而实现多种耦合效应下的数据解耦。其次，利用长短期记忆（Long Short-Term Memory, LSTM）网络构建深度学习架构，为每组分解后的监测数据建立预测模型，显著提升了预测精度。在此基础上，结合异常诊断理论与预测、实测数据，实现了桥梁结构安全动态预警系统。通过实测应变数据的验证案例表明，所提方法可准确预测桥梁应变数据并计算实时自适应阈值，实现异常监测数据的实时检测。