融合多源测量数据的桥梁挠度异常探测方法

桥梁是一种重要的交通基础设施,保障了人员和物资的流动运输,桥梁安全状态监测至关重要。受桥梁自身承重、桥面移动荷载和环境温度等因素的影响,桥梁挠度不断发生变化,而挠度异常可能会引起桥梁的结构性损伤而产生安全隐患。针对目前挠度异常探测方法没有综合考虑其外部环境和挠度自身变化特征的不足,本文提出了一种融合多源测量数据的桥梁挠度异常探测方法。利用环境温度、桥面移动荷载及桥梁挠度计算多源测量数据特征并融合,通过随机森林分类模型识别异常挠度。试验结果表明,本文方法的异常探测精度达到88.18%,效果良好,并且优于其他典型机器学习模型,能够帮助桥梁管理部门及时发现桥梁挠度异常情况,从而提高桥梁维护管理水平。

The method of bridge deflection outlier detection by fusing multi-sourced surveying data

Bridges are one of the most important transportation infrastructures as they guarantee the flow of people and goods, thus it is crucial for monitoring bridge safety. However, due to their intrinsic construction load as well as the extrinsic traffic load and environmental temperature, bridge deflection varies constantly. Moreover, the deflection outlier will cause a huge safety risk for bridges. The present detection methods for bridge deflection outlier still exist some limitations in the lack of synthetically considering both the extrinsic impact factors and the intrinsic variation features. Therefore, the paper proposes the detection method for bridge deflection outlier by fusing multi-sourced surveying data. It calculates and fuses the multi-sourced features based on temperature, bridge traffic load, and bridge deflection data. Besides, it utilizes the random forest model to judge whether the deflection is the outlier. The experimental results illustrate that the proposed method could get a good performance of the accuracy of 88.18%. In addition, the method's performance exceeds other classical machine learning models. In summary, the proposed method could help the bridge administrators detect the bridge deflection outlier to eliminate the safety risks, and further promote their maintenance and administration levels.