(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
Smart damped structures that embed viscoelastic layers and active elements are increasingly deployed in aerospace, civil, and automotive sectors to suppress vibration and extend service life. Their long-term resilience, however, depends on predictive maintenance strategies that can anticipate degradation of damping properties and structural integrity under stochastic operational loads. This paper presents a system-level integrated framework that couples high-fidelity digital twins with deep reinforcement learning agents to enable autonomous, condition-based maintenance planning for such complex mechatronic assemblies. The digital twin fuses physics-based modeling, in situ sensing, and continual model updating to mirror the evolving state of the structure, while the deep reinforcement learning module translates this state representation into maintenance policies that balance inspection cost, downtime, and residual life. We discuss the architecture and data-flow design of the twin–agent coupling, emphasizing modularity, real-time feedback, and edge-cloud orchestration. The analysis further examines structural trade-offs concerning governance, security, cross-organizational data sharing, and the robustness of decisions under epistemic and aleatory uncertainty. Policy dimensions, including fairness in asset fleet management and algorithmic accountability, are explored alongside implications for lifecycle sustainability and circular economy transitions. By integrating prognostics, control, and autonomous decision-making, the proposed framework offers a pathway toward self-aware damped structures that not only perceive their own health but also continuously optimize their maintenance in a resource-efficient and ethically grounded manner.