2022年12月13日
Deep reinforcement learning-based critical element identification and demolition planning of frame structures
Frontiers of Structural and Civil Engineering
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- 巻
- 16
- 号
- 開始ページ
- 1397
- 終了ページ
- 1414
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1007/s11709-022-0860-y
- 出版者・発行元
- Springer
This paper proposes a framework for critical element identification and demolition planning of frame structures. Innovative quantitative indices considering the severity of the ultimate collapse scenario are proposed using reinforcement learning and graph embedding. The action is defined as removing an element, and the state is described by integrating the joint and element features into a comprehensive feature vector for each element. By establishing the policy network, the agent outputs the Q value for each action after observing the state. Through numerical examples, it is confirmed that the trained agent can provide an accurate estimation of the Q values, and handle problems with different action spaces owing to utilization of graph embedding. Besides, different behaviors can be learned by varying hyperparameters in the reward function. By comparing the proposed method and the conventional sensitivity index-based methods, it is demonstrated that the computational cost is considerably reduced because the reinforcement learning model is trained offline. Besides, it is proved that the Q values produced by the reinforcement learning agent can make up for the deficiencies of existing indices, and can be directly used as the quantitative index for the decision-making for determining the most expected collapse scenario, i.e., the sequence of element removals.
- リンク情報
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- DOI
- https://doi.org/10.1007/s11709-022-0860-y 本文へのリンクあり
- ID情報
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- DOI : 10.1007/s11709-022-0860-y