Revolutionizing Canada's Aging Civil Infrastructure Assessment and Renewal: An Integrated, Multi-Scale, Remote Sensing, Data-Driven Approach

彻底改变加拿大老化的民用基础设施评估和更新：综合、多尺度、遥感、数据驱动的方法

• 批准号: RGPIN-2021-03597
• 负责人: Nehdi, Moncef
• 金额: $ 3.79万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749092
• 关键词: Revolutionizing; Canada; Aging; Civil; Infrastructure

Canada's civil infrastructure is the nation's most vital physical asset and is central to the quality of life of all Canadians. However, the most recent 2019 Report Card shows that our infrastructure is at risk, requiring considerable investments, while we still rely on outdated methods for infrastructure condition assessment and renewal. This is despite that Canada has been spending more on infrastructure renewal than on building new one; a trend expected to intensify in post-COVID-19 to mitigate a looming recession and ensure that lifeline systems can sustain crisis situations. Without creating a robust strategy to modernize Canada's infrastructure assessment and renewal, Canada's enormous investments in this area will be at risk, and competitors will control a related annual global market of US$ 10 trillion. This research program aims at reforming the condition assessment, damage diagnosis, design of repair strategies, and service life prognosis for aging concrete structures. It will surpass current subjective and operator sensitive visual inspection methods, to establish a rational, data-driven framework that integrates materials science and forensic engineering for robust condition assessment. Powerful machine learning algorithms using drone and robotic acquired big data will be deployed to deliver precise structural condition appraisal and prioritize aging structures for urgency of repair. Multi-scale and multi-source data will be integrated to identify the cause of structural damage. The machine learning framework will also be trained to identify effective repair strategies towards sustainable, resilient and economic infrastructure renewal in Canada. Big discovery often occurs at the crossroads of disciplines. Thus, this research will fuse synergistic knowledge areas and multi-scale data to invent strategies for infrastructure renewal. Remote sensing data will be acquired on aging concrete structures that are critical to national security or vulnerable to climate change effects. Machine learning algorithms will be trained as a robust intelligent framework for automated condition assessment to precisely identify and classify damage in these structures, allocate a global damage index, and prioritize structures for repair. This novel research program bridges critical knowledge gaps at the interface of materials science, forensic engineering and artificial intelligence to empower the inspector with robust portable decision-making tools for infrastructure appraisal and renewal. This transformative research will have pervasive impact via training Canada's future leaders on pioneering technology for infrastructure assessment and renewal, revolutionizing the condition assessment of aging structures, delivering faster and more precise results at lower cost, and allowing smarter resource allocation. The research will also empower the Canadian private sector to become a global leader with cutting-edge technology and robust gears in its toolkit.

加拿大的民事基础设施是美国最重要的物理资产，对于所有加拿大人的生活质量至关重要。但是，最近的2019年报告卡表明，我们的基础设施处于风险之中，需要大量投资，而我们仍然依靠过时的基础设施条件评估和续订的方法。尽管如此，加拿大在续签基础设施上的支出比建立新的花费更多。预计在199年后进行的一种趋势会加剧，以减轻迫在眉睫的衰退，并确保生命线系统能够维持危机情况。如果不制定强大的战略来使加拿大的基础设施评估现代化并更新，加拿大在这一领域的巨额投资将处于危险之中，竞争对手将控制一个相关的年度全球全球市场100万亿美元。该研究计划旨在改革状况评估，损害诊断，维修策略的设计以及对老化混凝土结构的使用寿命预后。它将超越当前的主观和操作员敏感的视觉检查方法，以建立一个合理的，数据驱动的框架，该框架整合了材料科学和法医工程以进行鲁棒状况评估。使用无人机和机器人获得的大数据进行的强大机器学习算法将被部署，以提供精确的结构性条件评估，并优先考虑衰老结构以进行紧迫的维修。多尺度和多源数据将集成以确定结构性损害的原因。机器学习框架还将进行培训，以确定加拿大可持续，弹性和经济基础设施更新的有效维修策略。大发现通常发生在学科的十字路口。因此，这项研究将融合协同的知识领域和多尺度数据，以发明基础设施更新策略。遥感数据将在对国家安全至关重要或容易受到气候变化影响的老化混凝土结构上获取。机器学习算法将作为自动条件评估的强大智能框架进行培训，以精确识别和分类这些结构中的损害，分配全球损害指数，并确定修复结构的优先级。这个新颖的研究计划弥合了材料科学，法医工程和人工智能的界面上的关键知识差距，以使用可靠的便携式决策工具为基础设施评估和续签增强了检查员的能力。这项变革性研究将通过培训加拿大未来的领导者对基础设施评估和更新的开创性技术产生普遍的影响，彻底改变了对衰老结构的状况评估，以较低的成本提供更快，更精确的结果，并允许更明智的资源分配。这项研究还将使加拿大私营部门能够在其工具包中以最先进的技术和强大的齿轮成为全球领导者。