Remote Infrastructure Monitoring Assessment via Multispectral Imaging of Surface Coatings

我国日益老化和日益恶化的基础设施产生了对变革性科学和技术的需求，这些科学和技术可以帮助采用新的管理和维护方法。该奖项支持在监测、测量、分析和计算方面的基础研究，以支持适用于民用基础设施的系统诊断。例子包括桥梁、铁路、砖石建筑、管道网络、输电线、大坝等。该奖项通过新型涂层研究多光谱成像，当沉积在实际结构部件上并由适当的成像设备监控时，这些涂层充当选择性响应传感单元。该奖项的结果可用于实施史无前例的结构健康监测程序，结合使用为在高动态环境中执行此类测量而开发的无人驾驶航空系统，有可能从根本上减少与漫长而昂贵的维护操作相关的停机时间。此外，它们可以提供更早和更定量地识别劣化和损害的能力。因此，这项研究的结果预计将有利于美国经济和社会，因为它的目标是在制造、监测和分析方法方面的重大改进，以及在城市系统、基础设施的可持续性和弹性方面的应用。这项研究的更广泛影响侧重于加强在恢复和改善城市基础设施方面取得贯穿各领域的进展的努力。在公民参与的背景下，研究将利用PIS机构合作教育计划中的资源，在教育课程中传授相关知识，并培训在系统诊断领域工作的下一代工程师。多模式遥感仪器平台与可扩展的制造和自主飞行器相结合，将创建一个框架，以快速评估基础设施系统的状况。这个项目的三项研究目标针对的是：利用设计材料方面的新制造程序和超材料方面的进展为可伸缩、可调谐、多模式、多光谱和嵌入式涂层创造前所未有的机会，进行自动检查和变形量化；利用配备多光谱传感器的无人机系统，能够通过询问涂层测量变形；开发方法，将多光谱图像点云及其他信息来源与多尺度和多模式建模结合起来，用于状态监测和剩余使用寿命的预测。具体地说，将采用新的制造工艺来设计表面涂层并将其嵌入结构部件。涂层的性能将进行调整，以确保通过捕捉紫外线、红外辐射和可见光的多光谱传感来快速检测。涂层元素的相对位置将使用基于计算机视觉和摄影测量的专用算法来跟踪，这些算法用于量化受监测结构的局部和全球位置坐标。

Our country's aging and deteriorating infrastructure creates the need for transformative science and technology that can assist in novel management and maintenance approaches. This award supports fundamental research on monitoring, measurement, analysis and computations in support of system diagnostics, applicable to a civil infrastructure. Examples include bridges, railroads, masonry buildings, networks of pipelines, powerlines, dams and more. This award investigates multispectral imaging through novel coatings that act as selectively responsive sensing units when deposited on actual structural components and monitored by appropriate imaging devices. The results of this award could be used to implement unprecedented structural health monitoring procedures which combined with the use of unmanned aerial systems developed to perform such measurements in highly dynamic environments have the potential to radically reduce downtime associated with lengthy and costly maintenance operations. In addition, they may offer the capability of both earlier and more quantitative identification of deterioration and damage. Therefore, results of this research are expected to benefit the U.S economy and society as it targets major improvements in manufacturing, monitoring and analysis methods with applications in urban systems, sustainability and resiliency of infrastructure. The broader impact of this research focuses in enhancing the efforts for cross-cutting progress in restoration and improvement of urban infrastructure. In this context of civic engagement, the research will leverage resources within the PIs' institution co-operative educational program to transfer related knowledge in educational curricula and train the next generation of engineers working in the area of system diagnostics.Multimodal remote sensing instrumentation platform coupled with scalable manufacturing and autonomous aerial vehicles will create a framework to rapidly assess the condition of infrastructure systems. Three research objectives in this project are targeted: the use of novel manufacturing procedures in a context of a materials-by-design and advances in metamaterials for the creation of unprecedented opportunities for scalable, tunable, multimodal, multispectral and embedded coatings for automated inspection and deformation quantification; the use of unmanned aerial systems equipped with multi-spectral sensors capable to measure deformation through interrogation of the coatings; the development of methodologies to combine multispectral image point clouds, as well as other sources of information with multiscale and multimodal modeling for state awareness and remaining useful life predictions. Specifically, novel manufacturing procedures will be adopted to design surface coatings and embed them in structural components. The properties of the coatings will be tailored to guarantee their rapid detection by multispectral sensing capturing ultraviolet and infrared radiation as well as visible light. The relative position of the coating elements will be tracked using dedicated algorithms based on computer vision and photogrammetry used to quantify local and global position coordinates of the monitored structures.