Interpretable Deep Reinforcement Learning for Element-Level Bridge Life-Cycle Optimization
Researchers have proposed a new approach to bridge life-cycle optimization using interpretable deep reinforcement learning. The approach uses element-level condition states (CS) to optimize bridge maintenance and repair. The authors demonstrated the effectiveness of their method on a real-world dataset and showed that it can reduce the cost of bridge maintenance while improving its reliability.
This development has the potential to improve the efficiency and effectiveness of bridge management systems.
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