Men Yanqing, Li Hu, Liu Fengzhou, Huang Yongliang, Gao Mingxin, Wang Xiaohui, Xie Hao, Cao Jianxin
Jinan Rail Transit Grp Co Ltd, Jinan, China.
Shandong Hi-speed Group Co Ltd, Jinan, China.
PLoS One. 2025 Jun 3;20(6):e0324816. doi: 10.1371/journal.pone.0324816. eCollection 2025.
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.
桥梁的结构响应涉及各种耦合效应之间复杂的相互作用，这使得识别长期变化趋势具有内在的挑战性。因此，在复杂耦合荷载作用下，有效检测和警示桥梁结构的异常监测数据仍然是一个重大难题。为解决这一问题，本研究提出一种基于数据重构和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（SVD）算法，根据影响因素的贡献率对监测数据进行分解和重构，从而将数据从各种耦合效应中解耦出来。其次，应用基于长短期记忆（LSTM）网络的深度学习架构，为每组分解后的监测数据建立预测模型，显著提高预测精度。在此基础上，将异常诊断理论与预测数据和实测数据相结合，实现了桥梁结构安全动态预警系统。一个使用实测应变数据的验证案例表明，所提出的方法能够准确预测桥梁应变数据并计算实时自适应阈值，从而实现对异常监测数据的实时检测。
