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

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2019 Report Card shows that our infrastructure is at risk, requiring considerable investments, while we still rely on outdated methods for infrastructure condition assessment and renewal.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