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

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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.