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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716748
• 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.

最近艾伯塔省洪水、不列颠哥伦比亚省野火和安大略省暴风雨等环境灾难的后果表明，加拿大的建筑环境(建筑、桥梁、管道)对自然灾害的适应能力很差。气候变化加剧了这些灾难；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自动化视觉的方法来了解我们基础设施中的风险的性质，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，可以以最小的资本和时间成本收集大量的可视数据(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材料劣化的明确迹象或自然灾害导致的结构损坏(形状变形、破裂、坍塌)的证据。根据可视数据识别这些变化提供了一种评估风险和及早发现即将发生的基础设施中断的新方法。 这项研究的目标是提供计算算法，通过将自动化集成到收集和分析可视数据的方法中来加速开发更安全、更具弹性的基础设施。将以面向任务的方式利用包含计算机视觉和深度学习算法的人工智能(AI)和计算平台，从基础设施中提取关键视觉指标，并在相关视觉数据中捕捉这些指标。从视觉数据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，监测它们在环境和运营变化下的恶化情况，并实现旨在填补阻碍现有建筑标准和设计规范的知识空白的数据驱动调查。 这项研究将带来许多好处，以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可视数据，以更好地评估我们的基础设施，并将为这些数据增加价值，供未来重复使用和相关的科学研究。此外，学员将在土木工程、人工智能和数据科学方面获得严格的多学科技能，为未来在这些行业和学术界就业做好准备。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方面的知识来解决广泛的土木工程问题的结构工程师提供新的机会。