Economic span selection of bridge based on deep reinforcement learning
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929414453854208 |
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| author | Zhang, Leye Tian, Xiangxiang Zhang, Chengli Zhang, Hongjun |
| author_facet | Zhang, Leye Tian, Xiangxiang Zhang, Chengli Zhang, Hongjun |
| contents | Deep Q-network algorithm is used to select economic span of bridge. Selection of bridge span has a significant impact on the total cost of bridge, and a reasonable selection of span can reduce engineering cost. Economic span of bridge is theoretically analyzed, and the theoretical solution formula of economic span is deduced. Construction process of bridge simulation environment is described in detail, including observation space, action space and reward function of the environment. Agent is constructed, convolutional neural network is used to approximate Q function,ε greedy policy is used for action selection, and experience replay is used for training. The test verifies that the agent can successfully learn optimal policy and realize economic span selection of bridge. This study provides a potential decision-making tool for bridge design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06507 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Economic span selection of bridge based on deep reinforcement learning Zhang, Leye Tian, Xiangxiang Zhang, Chengli Zhang, Hongjun Machine Learning Artificial Intelligence Deep Q-network algorithm is used to select economic span of bridge. Selection of bridge span has a significant impact on the total cost of bridge, and a reasonable selection of span can reduce engineering cost. Economic span of bridge is theoretically analyzed, and the theoretical solution formula of economic span is deduced. Construction process of bridge simulation environment is described in detail, including observation space, action space and reward function of the environment. Agent is constructed, convolutional neural network is used to approximate Q function,ε greedy policy is used for action selection, and experience replay is used for training. The test verifies that the agent can successfully learn optimal policy and realize economic span selection of bridge. This study provides a potential decision-making tool for bridge design. |
| title | Economic span selection of bridge based on deep reinforcement learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2407.06507 |