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2019 Report Card shows that our infrastructure is at risk, requiring considerable investments, while we still rely on outdated methods for infrastructure condition assessment and renewal.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.

加拿大的民用基础设施是国家最重要的有形资产，对所有加拿大人的生活质量至关重要。然而，最新的2019年报告卡显示，我们的基础设施面临风险，需要大量投资，而我们仍然依赖过时的基础设施状况评估和更新方法。尽管加拿大在基础设施更新方面的支出超过了建设新基础设施的支出，但这一趋势预计将在COVID-19后加剧，以缓解即将到来的经济衰退，并确保生命线系统能够承受危机局势。如果不制定一项强有力的战略来实现加拿大基础设施评估和更新的现代化，加拿大在这一领域的巨额投资将面临风险，竞争对手将控制每年10万亿美元的相关全球市场。本研究计划旨在改革老化混凝土结构的状态评估、损伤诊断、修复策略设计和使用寿命预测。它将超越目前的主观和操作员敏感的视觉检测方法，建立一个合理的，数据驱动的框架，整合材料科学和法医工程，以进行稳健的状态评估。将部署使用无人机和机器人获取的大数据的强大机器学习算法，以提供精确的结构状况评估，并优先考虑老化结构的维修紧急性。多尺度和多源数据将被整合，以确定结构损坏的原因。机器学习框架还将接受培训，以确定有效的修复策略，以实现加拿大可持续、有弹性和经济的基础设施更新。重大发现往往发生在学科的交叉点。因此，本研究将融合协同知识领域和多尺度数据，以发明基础设施更新策略。将获取对国家安全至关重要或易受气候变化影响的老化混凝土结构的遥感数据。机器学习算法将被训练为一个强大的智能框架，用于自动状态评估，以精确识别和分类这些结构中的损坏，分配全局损坏指数，并优先考虑修复结构。这一新颖的研究项目弥合了材料科学、法医工程和人工智能之间的关键知识差距，为检查员提供了用于基础设施评估和更新的强大便携式决策工具。这项变革性的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广泛的影响，彻底改变老化结构的状况评估，以更低的成本提供更快，更精确的结果，并允许更智能的资源分配。该研究还将使加拿大私营部门成为拥有尖端技术和强大工具的全球领导者。