Uncertainty Planning: Integrating Deep Uncertainty Into Resilience-Building in Transportation Infrastructure
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Cuadra, Manuel
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Abstract
This thesis proposes a planning method for transportation infrastructure that combines theories of Decision Making Under Deep Uncertainty (DMDU), and Resilience concepts. This decision is made on the premise that infrastructure environments are highly uncertain, and attempting to predict future conditions is not likely to be successful. Furthermore, complex systems with high inertia are often characterized by nonlinear and uncertain internal dynamics that make their behavior Deeply Uncertain. The thesis attempts to build Uncertainty Planning in three modules, and demonstrate its process and value using a case study.
The first module overviews DMDU principles, and applies them to a Top-Down method. This approach begins with system and operator goals, and plans a general strategy. From there, it deduces actions to take, optional adaptations to ensure its success (called supporting actions), and performance measures that guide action implementation (called key indicators).
The second module describes a Bottom-Up method that aims to satisfy the needs of disaggregate assets, and aggregate them into feasible overarching goals and strategies. This begins with understanding disaggregate hazards and condition metrics, from which patterns of common stresses are recorded, and inform programmatic adaptation projects.
The third module describes a micro-application of the process, which aims ensure individual project survival in scenarios possible within their lifecycles. This module stresses flexibility and the preservation of adaptation options.
The case study examines the Talmadge Memorial Bridge replacement. It first casts the replacement as a problem under Deep Uncertainty, and prescribes a planning strategy. By introducing and examining a simulation of the bridge replacement under various scenarios, the case study provides as series of recommendations for the asset’s continual management.
The thesis finishes with a review of its contributions and limitations. Notable throughout is that although literature cites effective practices, information limitations constrain the thoroughness of scenario and development path exploration, and condition relationships have to be significantly abstracted.
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2023-05-02
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