Li Jiaojiao, Meng Xiaolin, Hu Liangliang, Bao Yan
The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China.
Sensors (Basel). 2024 Mar 14;24(6):1877. doi: 10.3390/s24061877.
Long-span bridges are susceptible to damage, aging, and deformation in harsh environments for a long time. Therefore, structural health monitoring (SHM) systems need to be used for reasonable monitoring and maintenance. Among various indicators, bridge displacement is a crucial parameter reflecting the bridge's health condition. Due to the simultaneous bearing of multiple environmental loads on suspension bridges, determining the impact of different loads on displacement is beneficial for the better understanding of the health conditions of the bridges. Considering the fact that extreme gradient boosting (XGBoost) has higher prediction performance and robustness, the authors of this paper have developed a data-driven approach based on the XGBoost model to quantify the impact between different environmental loads and the displacement of a suspension bridge. Simultaneously, this study combined wavelet threshold (WT) denoising and the variational mode decomposition (VMD) method to conduct a modal decomposition of three-dimensional (3D) displacement, further investigating the interrelationships between different loads and bridge displacements. This model links wind speed, temperature, air pressure, and humidity with the 3D displacement response of the span using the bridge monitoring data provided by the GNSS and Earth Observation for Structural Health Monitoring (GeoSHM) system of the Forth Road Bridge (FRB) in the United Kingdom (UK), thus eliminating the temperature time-lag effect on displacement data. The effects of the different loads on the displacement are quantified individually with partial dependence plots (PDPs). Employing testing, it was found that the XGBoost model has a high predictive effect on the target variable of displacement. The analysis of quantification and correlation reveals that lateral displacement is primarily affected by same-direction wind, showing a clear positive correlation, and vertical displacement is mainly influenced by temperature and exhibits a negative correlation. Longitudinal displacement is jointly influenced by various environmental loads, showing a positive correlation with atmospheric pressure, temperature, and vertical wind and a negative correlation with longitudinal wind, lateral wind, and humidity. The results can guide bridge structural health monitoring in extreme weather to avoid accidents.
大跨度桥梁在恶劣环境中长期易受损伤、老化和变形影响。因此，需要使用结构健康监测（SHM）系统进行合理监测与维护。在各种指标中，桥梁位移是反映桥梁健康状况的关键参数。由于悬索桥同时承受多种环境荷载，确定不同荷载对位移的影响有助于更好地了解桥梁的健康状况。考虑到极端梯度提升（XGBoost）具有较高的预测性能和鲁棒性，本文作者开发了一种基于XGBoost模型的数据驱动方法，以量化不同环境荷载与悬索桥位移之间的影响。同时，本研究结合小波阈值（WT）去噪和变分模态分解（VMD）方法对三维（3D）位移进行模态分解，进一步研究不同荷载与桥梁位移之间的相互关系。该模型利用英国（UK）福斯路桥（FRB）的全球导航卫星系统（GNSS）和用于结构健康监测的地球观测（GeoSHM）系统提供的桥梁监测数据，将风速、温度、气压和湿度与桥跨的3D位移响应联系起来，从而消除温度对位移数据的时间滞后效应。使用部分依赖图（PDP）分别量化不同荷载对位移的影响。通过测试发现，XGBoost模型对位移目标变量具有较高的预测效果。量化和相关性分析表明，横向位移主要受同向风影响，呈现明显的正相关，垂直位移主要受温度影响，呈现负相关。纵向位移受多种环境荷载共同影响，与气压、温度和垂直风呈正相关，与纵向风、横向风和湿度呈负相关。研究结果可为极端天气下的桥梁结构健康监测提供指导，避免事故发生。
