基于计算机视觉的梅克伦堡大桥三维数字图像相关健康监测
Computer Vision-Based Health Monitoring of Mecklenburg Bridge Using 3D Digital Image Correlation
摘要：
弗吉尼亚大学（UVA）和弗吉尼亚州交通研究委员会之间的合作调查是在梅克伦堡大桥（I-85在梅克伦堡县1号公路）上进行的。该研究小组帮助弗吉尼亚州交通部-里士满区对其中一根桥梁的桥梁行为进行了表征，该桥梁由于之前的腹板屈曲和失稳失效而进行了修复。调查的重点是收集下降序列（拆除支座/桥墩上的支撑梁的千斤顶）期间的全场三维数字图像相关（3D-DIC）变形测量。此外，使用手持式激光扫描仪在跌落之前和之后对截面进行测量，以评估侧向变形或面外屈曲的可能性。研究结果表明，屈曲的测试梁没有发生，但确实提供了一系列的方法，可用于评估修复钢梁端部的有效性。具体而言，结果提供了一种方法，可以估计恒载分布，通过反算。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
