An interpretable RL framework generates auditable oblique decision tree policies for optimizing bridge life-cycles from element-level condition state proportions.
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An adaptive digital twin uses online Bayesian updates on transition probabilities in dynamic Bayesian networks, combined with reinforcement learning on parametric MDPs, to enable personalized predictive decision-making for structural health monitoring.
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Interpretable Deep Reinforcement Learning for Element-level Bridge Life-cycle Optimization
An interpretable RL framework generates auditable oblique decision tree policies for optimizing bridge life-cycles from element-level condition state proportions.
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Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics
An adaptive digital twin uses online Bayesian updates on transition probabilities in dynamic Bayesian networks, combined with reinforcement learning on parametric MDPs, to enable personalized predictive decision-making for structural health monitoring.