Enhancing Infrastructure Resiliency Through Visual Data Analytics

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The aftermath of recent environmental catastrophes - Alberta floods, British Columbia wildfires, and Ontario windstorms - reveals that our built environment (buildings, bridges, pipelines) in Canada is far from resilient to natural disasters. These disasters are exacerbated by climate change; therefore, it is critical to find solutions that improve community safety and resiliency. The proposed research uses an automated vision-based approach to understand the nature of the risk in our infrastructure and to enhance our preparedness for natural disasters. A large amount of visual data (images and videos) can be collected with minimal capital and time costs through advanced sensor and sensing platforms. They contain visual and geometric (2D/3D), and spatiotemporal (4D) information about the infrastructure before and after disaster events. Visual changes observed from these data provide clear indications of material deterioration or evidence of structural damage (shape deformation, cracking, collapsing) due to natural disasters. Identifying those changes from visual data provides a new means to assess risk and to enable early detection of impending infrastructure disruption. The goal of this research is to deliver the computational algorithm to accelerate the development of safer, more resilient infrastructure by integrating automation into the methods for collecting and analyzing visual data. Artificial intelligence (AI) encompassing computer vision and deep learning algorithms, and computing platforms will be leveraged in a task-oriented manner to extract critical visual indicators from infrastructure, which are captured in relevant visual data. The information collected from visual data will be used to assess communities' vulnerability and loss in natural disasters (flood, hurricane, earthquake), monitor their deteriorations under environmental and operational variations, and enable data-driven investigations that aim to fill the gaps in knowledge that hinder existing building standards and design codes. This research will deliver a number of benefits that address infrastructure challenges currently faced by Canadians and many others around the world. This program will empower engineers and researchers to capture the most valuable and useful visual data to better assess our infrastructure and will add value to that data for future reuse and relevant scientific study. Also, trainees will gain rigorous multidisciplinary skills in civil engineering, artificial intelligence, and data science, positioning them for future employment in these industries and academia. They will gain experience in an exciting emerging field, launching new opportunities for structural engineers seeking to exploit the body of knowledge in computer vision methods and data science to address a broad range of civil engineering problems.