K
K. G. Papakonstantinou
Researcher at Pennsylvania State University
Publications - 47
Citations - 952
K. G. Papakonstantinou is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Markov decision process & Partially observable Markov decision process. The author has an hindex of 10, co-authored 41 publications receiving 560 citations. Previous affiliations of K. G. Papakonstantinou include Columbia University & University of California, Irvine.
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Probabilistic model for steel corrosion in reinforced concrete structures of large dimensions considering crack effects
TL;DR: A time-dependent model is developed that can simulate all stages of reinforced concrete corrosion, i.e. corrosion initiation, crack initiation and propagation, and the extent of damage is quantified by considering the spatial variability of the various parameters.
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Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part II: POMDP implementation
TL;DR: In this second part of the study a distinct, advanced, infinite horizon POMDP formulation with 332 states is cast and solved, related to a corroding reinforced concrete structure and its minimum life-cycle cost.
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Managing engineering systems with large state and action spaces through deep reinforcement learning
TL;DR: The Deep Centralized Multi-agent Actor Critic (DCMAC) as discussed by the authors is an off-policy actor-critic DRL algorithm that directly probes the state/belief space of the underlying MDP/POMDP, providing efficient life-cycle policies for large multi-component systems operating in high-dimensional spaces.
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Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part I: Theory
TL;DR: The overall objective of this two part study is to highlight the advanced attributes, capabilities and use of stochastic control techniques, and especially Partially Observable Markov Decision Processes (POMDPs) that can address the conundrum of planning optimum inspection/monitoring and maintenance policies based on stoChastic models and uncertain structural data in real time.
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Optimum inspection and maintenance policies for corroded structures using partially observable Markov decision processes and stochastic, physically based models
TL;DR: In this work the effort is mostly based in modeling and solving the problem of finding optimal policies for the maintenance and management of aging structures through a POMDP framework with large state spaces that can adequately and sufficiently describe real-life problems.