98 citations · 191 across the 6 of their papers we have counts for
10 papers
Graph-Based Inference and Topology-Aware Multi-Agent Reinforcement Learning for Large-Scale Railway Network Management
Giacomo Arcieri, Gregory Duthé, Christophe Muller +3
Modern infrastructure asset management constitutes a complex sequential decision-making problem, characterized by long planning horizons and system-level interactions, such as spat…
POMDP inference and robust solution via deep reinforcement learning: An application to railway optimal maintenance
Giacomo Arcieri, Cyprien Hoelzl, Oliver Schwery +3
Partially Observable Markov Decision Processes (POMDPs) can model complex sequential decision-making problems under stochastic and uncertain environments. A main reason hindering t…
Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems
Giacomo Arcieri, Cyprien Hoelzl, Oliver Schwery +3
Structural Health Monitoring (SHM) describes a process for inferring quantifiable metrics of structural condition, which can serve as input to support decisions on the operation an…
Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning
Pablo G. Morato, Charalampos P. Andriotis, Konstantinos G. Papakonstantinou +1
In the context of modern environmental and societal concerns, there is an increasing demand for methods able to identify management strategies for civil engineering systems, minimi…
Optimal Inspection and Maintenance Planning for Deteriorating Structural Components through Dynamic Bayesian Networks and Markov Decision Processes
P. G. Morato, K. G. Papakonstantinou, C. P. Andriotis +2
Civil and maritime engineering systems, among others, from bridges to offshore platforms and wind turbines, must be efficiently managed as they are exposed to deterioration mechani…
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints
C. P. Andriotis, K. G. Papakonstantinou
Determination of inspection and maintenance policies for minimizing long-term risks and costs in deteriorating engineering environments constitutes a complex optimization problem.…
Hamiltonian MCMC methods for estimating rare events probabilities in high-dimensional problems
Konstantinos G. Papakonstantinou, Hamed Nikbakht, Elsayed Eshra
Accurate and efficient estimation of rare events probabilities is of significant importance, since often the occurrences of such events have widespread impacts. The focus in this w…
Value of structural health information in partially observable stochastic environments
C. P. Andriotis, K. G. Papakonstantinou, E. N. Chatzi
Efficient integration of uncertain observations with decision-making optimization is key for prescribing informed intervention actions, able to preserve structural safety of deteri…
A direct Hamiltonian MCMC approach for reliability estimation
Hamed Nikbakht, Konstantinos G. Papakonstantinou
Accurate and efficient estimation of rare events probabilities is of significant importance, since often the occurrences of such events have widespread impacts. The focus in this w…
Managing engineering systems with large state and action spaces through deep reinforcement learning
C. P. Andriotis, K. G. Papakonstantinou
Decision-making for engineering systems can be efficiently formulated as a Markov Decision Process (MDP) or a Partially Observable MDP (POMDP). Typical MDP and POMDP solution proce…