Load-structure adaptation: A reward-driven mechanism for cellular structure self-reconfiguration

Load-Structure Adaptation is proposed in this study as a reward-driven mechanism for developing a self-reconfiguration decision-making system for cellular structures. By integrating neural networks and reinforcement learning methods, a time-constrained reconfiguration path planning approach for cellular structures under imminent loads is established, and a decision matrix traversing the global state space is constructed. The results demonstrate that the protective effectiveness of cellular structures under blast loading critically depends on the adaptation between structural characteristics and load time-frequency features, enabling performance exceeding inherent structural limits. Once load variables are introduced, protection can no longer be evaluated solely by inherent structural properties. A dimensionless response ratio serves as a unified evaluator: it follows a universal form in the elastic phase, while in the plastic phase, three expressions are derived based on the four Load-Structure Types, allowing assessment of either load damage potential or structural protection capacity. The reconfiguration path represents a real-time optimal trajectory toward the global optimum, with each step maximizing the instantaneous protective benefit. For different load characteristics (peak pressure, duration, energy concentration band), the corresponding optimal structural state and its locally optimal path vary: in the elastic phase they are chiefly governed by load duration and energy concentration frequency, whereas in the plastic phase they are determined by the coupled effect of peak pressure and duration. Under a series of idealized yet reasonable assumptions, this study provides a complete decision-making framework from theory to design for the pre-adjustment of cellular structures in dynamic threat environments, such as explosive blasts.