Enhancing Infrastructure Resiliency Through Visual Data Analytics

通过可视化数据分析增强基础设施的弹性

• 批准号: RGPIN-2020-03979
• 负责人: Yeum, ChulMin
• 金额: $ 1.89万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739602
• 关键词: Enhancing; Infrastructure; Resiliency; Through; Visual

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.

最近的环境灾难--阿尔伯塔洪水、不列颠哥伦比亚省野火和安大略风暴--的后果表明，我们加拿大的建筑环境（建筑、桥梁、管道）对自然灾害的适应能力远远不够。这些灾害因气候变化而加剧;因此，找到改善社区安全和复原力的解决方案至关重要。拟议的研究使用基于视觉的自动化方法来了解我们基础设施中的风险性质，并加强我们对自然灾害的准备。 通过先进的传感器和传感平台，可以以最小的资金和时间成本收集大量的视觉数据（图像和视频）。它们包含关于灾害事件前后基础设施的视觉和几何（2D/3D）以及时空（4D）信息。从这些数据中观察到的视觉变化提供了由于自然灾害导致的材料退化或结构损坏（形状变形，开裂，倒塌）的明确迹象。从视觉数据中识别这些变化提供了一种新的手段来评估风险，并能够早期检测即将发生的基础设施中断。 本研究的目标是通过将自动化集成到收集和分析视觉数据的方法中，提供计算算法，以加速更安全，更具弹性的基础设施的开发。人工智能（AI）包括计算机视觉和深度学习算法，以及计算平台将以面向任务的方式从基础设施中提取关键的视觉指标，这些指标将被捕获在相关的视觉数据中。从视觉数据中收集的信息将用于评估社区在自然灾害（洪水、飓风、地震）中的脆弱性和损失，监测其在环境和业务变化下的恶化情况，并促进数据驱动的调查，以填补阻碍现有建筑标准和设计规范的知识空白。 这项研究将带来许多好处，解决加拿大人和世界各地许多其他人目前面临的基础设施挑战。该计划将使工程师和研究人员能够捕获最有价值和最有用的视觉数据，以更好地评估我们的基础设施，并将为未来的重用和相关的科学研究增加数据的价值。此外，学员将获得土木工程，人工智能和数据科学方面严格的多学科技能，为他们未来在这些行业和学术界的就业做好准备。他们将在一个令人兴奋的新兴领域获得经验，为寻求利用计算机视觉方法和数据科学知识体系的结构工程师提供新的机会，以解决广泛的土木工程问题。