Sustainable Management of Infrastructure Assets and Networks Under Uncertainty

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With nearly 40% of municipal roadways in a fair or worse condition, Canada faces tremendous challenges to maintain its aging infrastructure. Worth $330 billion, Canada's roadway systems is its highest valued municipal asset class. Federal, provincial, and municipal agencies increasingly rely on pavement management systems, which are quantitative decision-support tools, to generate sustainable design, construction, and maintenance strategies. The determination of a cost-effective and environmentally-sustainable pavement management plan requires the consideration of factors such as an asset's rate of deterioration, the evolution of traffic patterns, and availability of future fiscal budgets that are inherently unknown. Despite this reality, the prevailing paradigms found in practice assume a known future which simplifies the analysis and makes a complex problem tractable. However, this assumption leads to the selection of static resource allocation policies, leaving planners ill-equipped to adapt their decisions when circumstances evolve in unforeseen ways. This research program aims to dramatically enhance current pavement management practices by developing a suite of probabilistic software tools that municipal and provincial agencies across Canada can leverage to select robust, adaptable resource-allocation policies for their roadway assets. Specifically, I propose to develop novel software tools that enhance the current project-level models that planners use to move forward with large-scale infrastructure projects, and to advance network-level systems, which support allocation decisions across a network of facilities. For each of these contexts, planners must make decisions that balance between cost, environmental impact, and infrastructure durability. Because these pavement management tools must be able to scale computationally for the size and complexity of situations found in practice, we aim to generate new knowledge on the computational performance and fidelity of alternative heuristic approaches. My research program has successfully collaborated with three state departments of transportation in the United States (Colorado, Minnesota, and Virginia) to create probabilistic asset management tools that enable the agencies to achieve their objectives at significantly reduced costs. These different collaborative initiatives have illustrated that the incorporation of uncertainty creates new opportunities to improve the management of infrastructure systems. We now aim to advance our basic understanding of these probabilistic frameworks and leverage them to improve decision-making across various objectives and scales. This project will prepare undergraduate and graduate students to be the future engineers and researchers charged with directing Canada's infrastructure assets.