United States Department of Transportation
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Deep Reinforcement Learning for Multi-asset Infrastructure Management Incorporating Traffic Operations Adaptations and Control
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2023-04-01
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[PDF-5.40 MB]
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Abstract:Preserving structural integrity through inspections and maintenance (I&M) is a critical and recurrent task that involves complex decisions strategically placed in both space and time to ensure the longevity and reliability of infrastructure assets. At the regional scale, the optimal management of entire networks amplifies the complexity of these decisions, primarily due to the vast number of interconnected assets, each with its inherent dependencies and correlations. In the context of transportation networks, the dependencies can manifest as system-level effects on traffic flow, as the individual contributions of various assets to traffic capacity collectively influence system performance, often in a nonlinear manner. Consequently, assessing the potential impacts of different combinations of I&M decisions, each linked to a specific asset, becomes an intricate process.
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