论文标题
使用3D数字图像相关性对Mecklenburg桥的基于计算机视觉的健康监测
Computer Vision-Based Health Monitoring of Mecklenburg Bridge Using 3D Digital Image Correlation
论文作者
论文摘要
弗吉尼亚大学(UVA)和弗吉尼亚州运输研究委员会的合作调查是在梅克伦堡桥(Mecklenburg Bridge)(梅克伦堡县1号公路上的I-85)进行的。研究小组帮助弗吉尼亚州交通运输部 - 里士满区,表征了由于先前的网络屈曲和严重的失败而修复的桥梁横梁的桥梁行为。该研究的重点是收集全场三维数字图像相关性(3D-DIC)变形测量值(在掉落序列上(去除千斤顶)以支撑轴承/码头上的光束)。此外,使用手持激光扫描仪在掉落前后进行了测量,以评估横向变形或平面外屈曲的潜力。研究的结果表明,未发生测试束的屈曲,但确实提供了一系列可用于评估修复钢束末端的有效性的方法。具体而言,结果提供了一种方法,可以通过背面计算来估计死负荷分布。
A collaborative investigation between the University of Virginia (UVA) and the Virginia Transportation Research Council was performed on the Mecklenburg Bridge (I-85 over Route 1 in Mecklenburg County). The research team aided the Virginia Department of Transportation - Richmond District in the characterization of the bridge behavior of one of the bridge beams that had been repaired due to a previous web buckling and crippling failure. The investigation focused on collecting full-field three-dimensional digital image correlation (3D-DIC) deformation measurements during the dropping sequence (removal of jacking to support beam on bearing/pier). Additionally, measurements were taken of the section prior to and after dropping using a handheld laser scanner to assess the potential of lateral deformation or out-of-plane buckling. Results from the study demonstrated that buckling of the tested beam did not occur, but did provided a series of approaches that can be used to evaluate the effectiveness of repaired steel beam ends. Specifically, the results provided an approach that could estimate the dead load distribution through back-calculation.