EAGER: Active Citizen Engagement to Enable Lifecycle Management of Infrastructure Systems

结构中的缺陷和缺陷在结构的生命周期中逐渐演变，必须定期评估潜在的退化，以做出合理的管理决策并设置修复优先级。人类观察仍然是这种评价的主要机制，既耗时又昂贵。公民科学和众包提供了从许多角度、频繁间隔和在许多条件下收集大量特定结构照片的机会。这种可视数据跨越时间和空间，能够详细记录随时间推移的退化情况。这个探索性研究（EAGER）项目将利用计算机视觉方面的最新知识来自动化许多与生命周期结构评估和管理相关的任务，从而降低生命周期成本和风险。该项目中开发的方法也将为寻求利用数据科学来解决广泛的结构工程问题的结构工程师提供新的机会。我们将制作一个流媒体视频，演示我们的目标结构的方法，以便广泛传播和学生参与。没有受过工程师训练的公民的日常图像与工程师捕获的图像类型非常不同。这些图像的大部分信息与工程目的无关，这些图像的自动化处理将产生错误的结论。此外，这些图像是从随机的位置和角度收集的，缺乏尺度和方向信息。然而，这些障碍是可以克服的。该项目将纳入有关结构评估过程的基本知识，以便将这些图像用于工程目的。每个查询图像与目标结构模型之间的几何关系将通过匹配它们的局部特征来计算。对每幅图像进行目标结构的自动定位，并提取图像的相关部分，称为感兴趣区域，供人或机器进行结构评估。实验验证将使用通过社交媒体从积极参与的公民收集的图像进行。将对该方法的能力进行定量评价，特别是针对结构的脆弱区域。该项目将克服使用来自公民科学家的可视化数据的固有挑战，促进我们如何执行生命周期结构评估的转变。

Flaws and defects in structures evolve gradually over a structure's lifetime, and potential degradation must be evaluated periodically to make sound management decisions and set repair priorities. Human observation, still the predominant mechanism for such evaluation, is time-consuming and costly. Citizen science and crowdsourcing provide opportunities to collect large numbers of photos of certain structures, from many perspectives, at frequent intervals, and under many conditions. Such visual data reach across both time and space, enabling a detailed record of deterioration over time. This EArly-concept Grant for Exploratory Research (EAGER) project will exploit the latest knowledge in computer vision to automate many tasks related to lifecycle structural evaluation and management, reducing both lifecycle cost and risk. The methodology developed within this project will also launch new opportunities for structural engineers seeking to exploit data science to address a broad range of structural engineering problems. A streaming video demonstrating the methodology on our target structure will be developed for broad dissemination and student engagement. Everyday images from citizens, not trained as engineers, are very different than the types of images than engineers capture. Large portions of these images have information irrelevant for engineering purposes and automated processing of these images would generate faulty conclusions. Furthermore, these images are collected from random locations and perspectives, and lack scale and orientation information. However, these barriers can be overcome. This project will incorporate essential knowledge about the structural evaluation process to enable the use of these images for engineering purposes. Geometric relationships between each of the query images and the model of the target structure will be computed by matching their local features. Automatic localization of each of these images with respect to the target structure will be performed, and relevant portion of the images, called the region of interest, will be extracted for structural evaluation by human or machine. Experimental validation will be performed using images collected from active engaged citizens through social media. A quantitative evaluation of the capabilities of the methodology will be performed, targeting especially vulnerable regions of a structure. This project will overcome the inherent challenges in using visual data from citizen scientists, facilitating a transformation in how we perform lifecycle structural evaluation.