Identifying damage on a bridge using rotation-based Bridge Weigh-In-Motion

JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING(2020)

引用 21|浏览10
暂无评分
摘要
Bridge Weigh-in-Motion (B-WIM) systems use the bridge response under a traversing vehicle to estimate its axle weights. The information obtained from B-WIM systems has been used for a wide range of applications such as pre-selection for weight enforcement, traffic management/planning and for bridge and pavement design. However, it is less often used for bridge condition assessment purposes which is the main focus of this study. This paper presents a bridge damage detection concept using information provided by B-WIM systems. However, conventional B-WIM systems use strain measurements which are not sensitive to local damage. In this paper the authors present a B-WIM formulation that uses rotation measurements obtained at the bridge supports. There is a linear relationship between support rotation and axle weight and, unlike strain, rotation is sensitive to damage anywhere in the bridge. Initially, the sensitivity of rotation to damage is investigated using a hypothetical simply supported bridge model. Having seen that rotation is damage-sensitive, the influence of bridge damage on weight predictions is analysed. It is shown that if damage occurs, a rotation-based B-WIM system will continuously overestimate the weight of traversing vehicles. Finally, the statistical repeatability of ambient traffic is studied using real traffic data obtained from a Weigh-in-Motion site in the U.S. under the Federal Highway Administration’s Long-Term Pavement Performance programme and a damage indicator is proposed as the change in the mean weights of ambient traffic data. To test the robustness of the proposed damage detection methodology numerical analysis are carried out on a simply supported bridge model and results are presented within the scope of this study.
更多
查看译文
关键词
Bridge,Damage detection,Rotation,Bridge weigh-in-motion,B-WIM,WIM,Ambient traffic
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要