RAPID: Data Fusion for Structural Assessment of the Fern Hollow Bridge Replacement During Construction

最近，宾夕法尼亚州匹兹堡的Fern Hollow大桥坍塌，凸显了在运营国家大型互联基础设施时，需要采取积极主动的评估和维护策略。这项快速反应研究（Rapid）拨款将侧重于在每个主要施工阶段获得桥梁更换的高保真三维模型。每个顺序扫描将在相同的参考系统中收集，以允许连续重建进行定量比较。由于景观将迅速演变，这些独特的数据将允许分析评估结构响应的快节奏动态变化。所获得的数据和相关分析带来了极大的潜力，有助于进一步的技术发展，以设计快速施工，以及有效的桥梁监测和结构系统的非破坏性评估。该项目将通过创造新的科学和技术(i)通过不同的传感技术从桥梁获取高分辨率数据，（ii）数据融合方法来解释这些信息，以及（iii）将这些信息转化为随时间变化的抵抗元素的结构评估，从而造福社会。这项研究有明显的潜力，可以作为及时改善老化基础设施维护的基准，同时也为倒塌桥梁的重建提供有价值的信息。研究的具体目标是表征预应力钢筋混凝土桥梁在施工过程中结构响应的时变演变。这些数据将通过安装在无人机系统上的激光传感器和摄像传感器来获取。同时定位和映射算法将用于两个数据集的融合和随后的三维模型重建。研究假设：利用激光测量的高空间分辨率和相机传感器获得的颜色信息，通过数据融合算法获得的更丰富的数据集，可以对重构场景进行更准确的语义分割。该项目为推进测量技术的使用和应用以及这种技术与逆分析方法的结合提供了机会。这些数据将直接用于创建新的“扫描到分析”数字工具，并在预应力钢筋混凝土桥梁的结构健康监测和预测方面取得显著进展，特别注意将施工信息纳入系统评估。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The recent collapse of the Fern Hollow bridge in Pittsburgh, Pennsylvania, highlighted how proactive assessment and maintenance strategies are required to operate the large, interconnected infrastructure of the country. This Grant for Rapid Response Research (RAPID) award will focus on obtaining high-fidelity three-dimensional models of the bridge replacement throughout every major construction phase. Each sequential scan will be collected in the same reference system, to allow for consecutive reconstructions to be compared quantitatively. Since the landscape will evolve rapidly, this unique data will allow for analyses to assess the fast-paced dynamic changes in structural response. The obtained data and the related analyses bring the potential to significantly aid further technology development towards design for rapid construction as well as efficient bridge monitoring and non-destructive assessment of structural systems. This project will benefit society by creating novel science and technology for (i) the acquisition of high-resolution data from a bridge by means of different sensing techniques, (ii) data fusion approaches to interpret such information, and (iii) the translation of such information to structural assessment of the resisting elements over time. There is clear potential for this research to serve as a benchmark to improve the maintenance of aging infrastructure in a timely fashion, while also providing valuable information on the reconstruction of a collapsed bridge.The specific goal of the research is to characterize the time-dependent evolution of the structural response in pre-stressed reinforced concrete bridges during construction. The data will be acquired by the use of both laser-based and camera sensors mounted on unmanned aerial systems. Simultaneous Localization and Mapping algorithms will be employed for the fusion of the two datasets and subsequent reconstruction of the three-dimensional models. The research hypothesis is that the richer datasets obtained by means of the data fusion algorithms will enable more accurate semantic segmentation of the reconstructed scenes, by leveraging the high spatial resolution of the laser-based measurement and the color information obtained from the camera sensors. This project provides the opportunity to advance both the use and application of measurement technology and the combination of such technology with inverse analysis methods. The data will be directly usable to create novel “scan-to-analysis” digital tools and enable remarkable advancements in structural health monitoring and prognosis of pre-stressed reinforced concrete bridges, with specific attention devoted to incorporating construction information in the system assessment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.