融合多源测量数据的桥梁挠度异常探测方法  

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

作　　者：陈睿哲 涂伟[1,2,3] 李清泉[1,2,3] 谷宇 左小清[4] 高文武 CHEN Ruizhe;TU Wei;LI Qingquan;GU Yu;ZUO Xiaoqing;GAO Wenwu(Guangdong Key Laboratory of Urban Informatics,Shenzhen University,Shenzhen 518060,China;Ministry of Natural Resources Key Laboratory for Geo-Environmental Monitoring of Great Bay Area,Shenzhen University,Shenzhen 518060,China;Shenzhen Key Laboratory of Spatial Smart Sensing and Services,Shenzhen University,Shenzhen 518060,China;Faculty of Land Resource Engineering,Kunming University of Science and Technology,Kunming 650093,China;Xi’an Min-Win M&C Technology Co.,Ltd.,Xi'an 710199,China)

机构地区：[1]深圳大学广东省城市空间信息工程重点实验室,广东深圳518060 [2]深圳大学自然资源部大湾区地理环境监测重点实验室,广东深圳518060 [3]深圳大学深圳市空间信息智能感知与服务重点实验室,广东深圳518060 [4]昆明理工大学国土资源工程学院,云南昆明650093 [5]西安敏文测控科技有限公司,陕西西安710199

出　　处：《测绘通报》2022年第9期105-110,共6页Bulletin of Surveying and Mapping

基　　金：国家自然科学基金高铁联合基金(U1934215);深圳市科创委技术攻关项目(JSGG20201103093401004)。

摘　　要：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。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.

关 键 词：桥梁挠度 异常探测 数据融合 随机森林 

分 类 号：P258[天文地球—测绘科学与技术]