Inference and maintenance planning of monitored structures through Markov chain Monte Carlo and deep reinforcement learning

A key computational challenge in maintenance planning for deteriorating structures is to concurrently secure (i) optimality of decisions over long planning horizons, and (ii) accuracy of realtime parameter updates in high-dimensional stochastic spaces. Both are often encumbered by the presence of discretized continuous-state models that describe the underlying deterioration processes, and the emergence of combinatorial decision spaces due to multi-component environments. Recent advances in Deep Reinforcement Learning (DRL) formulations for inspection and maintenance planning provide us with powerful frameworks to handle efficiently near-optimal decision-making in immense state and action spaces without the need for offline system knowledge. Moreover, Bayesian Model Updating (BMU), aided by advanced sampling methods, allows us to address dimensionality and accuracy issues related to discretized degradation processes. Building upon these concepts, we develop a joint framework in this work, coupling DRL, more specifically deep Q-learning and actor-critic algorithms, with BMU through Hamiltonian Monte Carlo. Single- and multi-component systems are examined, and it is shown that the proposed methodology yields reduced lifelong maintenance costs, and policies of high fidelity and sophistication compared to traditional optimized time- and condition-based maintenance strategies.