Keeping Infrastructure Safe During the COVID-19 Pandemic using Artificial Intelligence

使用人工智能在 COVID-19 大流行期间确保基础设施安全

• 批准号: 555258-2020
• 负责人: Billah, AbuHenaMdMuntasir
• 金额: $ 3.63万
• 依托单位: Lakehead University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 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=705751
• 关键词: Keeping; Infrastructure; Safe; During; COVID

Keeping our infrastructure (bridges, roads, buildings) in safe and operational condition is a major challenge. The sudden pause in engineering and construction sector due to the COVID-19 pandemic can elevate the risk of unwanted accidents and mishaps above normal. When the safety is compromised, the overall infrastructure system becomes ineffective and public safety is under immense threat. To avoid such scenarios, the need for an efficient infrastructure management system cannot be overstated. The proposed research is designed to demonstrate how data analytics and machine learning models can help infrastructure owners and managers predict the condition of existing structures during the lockdown phase of this pandemic, using already available inspection and as-built data. This will aid in identifying safety hazards present during lockdowns at all engineered sites, whether for buildings, bridges or other infrastructure. Using COVID-19 spread and disruption data in a machine learning algorithm, this study will investigate how lockdowns along with revised ongoing-work methods or changed supervision practices could affect structural safety of Canadian infrastructure. This research aims to develop a decision support tool to enable risk-informed critical infrastructure identification based on possible cascade effects for addressing issues pertaining to aging and deteriorating infrastructure during future lockdowns. Combining the expertise of the applicant along with the experienced industry personnel from TBT Engineering Ltd., this project will develop an advanced tool with respect to enhance decision making on critical infrastructure to increase safety and decrease unexpected shutdown and/or failure. This research will also help TBTE to provide specialized services, which will play a significant role in the Canadian infrastructure industry to manage such emergencies during pandemic. The successful completion of the proposed research will pave the way toward resiliency of Canada's infrastructure network against the uncertainty arising from the lockdown.

保持我们的基础设施(桥梁、道路、建筑物)处于安全和运行状态是一项重大挑战。新冠肺炎疫情导致工程和建筑行业突然停顿，可能会使不必要的事故和灾难的风险高于正常水平。当安全受到损害时，整个基础设施系统变得无效，公共安全受到巨大威胁。为了避免这种情况，对有效的基础设施管理系统的需求怎么强调都不为过。拟议的研究旨在展示数据分析和机器学习模型如何使用已有的检查和竣工数据，帮助基础设施所有者和管理人员在此次大流行的封锁阶段预测现有建筑物的状况。这将有助于识别所有工程地点封锁期间存在的安全隐患，无论是建筑物、桥梁还是其他基础设施。这项研究将在机器学习算法中使用新冠肺炎传播和中断数据，调查封锁以及修订的持续工作方法或改变的监管做法可能如何影响加拿大基础设施的结构安全。这项研究旨在开发一种决策支持工具，以实现基于可能的级联效应的风险知情的关键基础设施识别，以解决与未来封锁期间的基础设施老化和恶化有关的问题。该项目结合了申请者的专业知识以及TBT工程有限公司的经验丰富的行业人员，将开发一种先进的工具，以加强关键基础设施的决策，以提高安全性，减少意外停机和/或故障。这项研究还将帮助TBTE提供专门的服务，这将在加拿大基础设施行业中发挥重要作用，以管理疫情期间的此类紧急情况。拟议研究的成功完成将为加拿大基础设施网络应对封锁带来的不确定性铺平道路。