Towards Quantitative Integration of Reliability and Resiliency into Roadway Design: AI-Aided Road Vulnerability Assessment and Stochastic Uncertainty Modelling

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Canada is home to one of the world's largest road networks. These roads are essential to the economic prosperity and mobility of Canadians. Assessing and enhancing the reliability and resiliency of road infrastructure is a timely and critical issue to transportation agencies due to the unprecedented challenges resulting from climate change (e.g. handling mass evacuation after a natural disaster), changes in road user demographics (e.g. accommodating an aging population), and the introduction of new forms of mobility (e.g. self-driving cars). To ensure that roads are prepared for, and able to handle, such challenges, there is demand for efficient and accurate methods to assess resiliency of existing roads. There is also a need for metrics for quantitative integration of resiliency into design. The long-term objective of my research is to transform the process of designing and managing road infrastructure into one that is data driven and resilient against uncertain events. I propose 3 objectives over the next 5 years to address critical knowledge gaps. First, I will develop novel Artificial Intelligence (AI) algorithms for efficient extraction of resiliency-critical road features from Light Detection and Ranging (LiDAR) and street-level imagery. LiDAR is a form of remote sensing that creates virtual 3D models (point clouds) of roads by driving a truck-mounted laser scanner along a road. Building on my past work, which has been utilized by municipal and provincial agencies, algorithms I develop in this program will employ a novel multi-scale segmentation strategy to automatically extract resiliency-critical features for proactive road assessment. Second, I will use statistical simulation to model uncertainty and assess reliability of existing road design elements extracted using the AI algorithms. I will use simulation results to propose novel resiliency performance indicators and design charts for quantitative integration of reliability into design. This research is pioneering since, to date, there are no means of quantitatively integrating reliability and resiliency into design in existing standards. Under the final objective I will use spatial statistics to assess road resiliency on an aggregate network level. Although segment-level reliability has been assessed, a network-level assessment is unprecedented. The proposed work will present Canadian transport agencies with new metrics and novel algorithms for proactive assessment of road resiliency and vulnerability. Besides facilitating resiliency assessments of existing roads, research outputs will establish standards for comparing resiliency of new design alternatives. This helps design more resilient roads and optimize the use of limited public funds when managing existing infrastructure. In this program I will train 2 PhDs, 2 MScs, and 5 Undergraduates in areas of machine learning, AI, statistical modeling, and GIS, which are highly sought-after skills in the Engineering market.