ダウンロード数: 137
このアイテムのファイル:
ファイル | 記述 | サイズ | フォーマット | |
---|---|---|---|---|
fbuil.2020.00059.pdf | 2.08 MB | Adobe PDF | 見る/開く |
タイトル: | Reinforcement Learning and Graph Embedding for Binary Truss Topology Optimization Under Stress and Displacement Constraints |
著者: | Hayashi, Kazuki https://orcid.org/0000-0002-4026-8234 (unconfirmed) Ohsaki, Makoto https://orcid.org/0000-0003-4935-8874 (unconfirmed) |
著者名の別形: | 林, 和希 大﨑, 純 |
キーワード: | topology optimization binary-type approach machine learning reinforcement learning graph embedding truss stress and displacement constraints |
発行日: | 2020 |
出版者: | Frontiers Media S.A. |
誌名: | Frontiers in Built Environment |
巻: | 6 |
論文番号: | 59 |
抄録: | This paper addresses a combined method of reinforcement learning and graph embedding for binary topology optimization of trusses to minimize total structural volume under stress and displacement constraints. Although conventional deep learning methods owe their success to a convolutional neural network that is capable of capturing higher level latent information from pixels, the convolution is difficult to apply to discrete structures due to their irregular connectivity. Instead, a method based on graph embedding is proposed here to extract the features of bar members. This way, all the members have a feature vector with the same size representing their neighbor information such as connectivity and force flows from the loaded nodes to the supports. The features are used to implement reinforcement learning where an action taker called agent is trained to sequentially eliminate unnecessary members from Level-1 ground structure, where all neighboring nodes are connected by members. The trained agent is capable of finding sub-optimal solutions at a low computational cost, and it is reusable to other trusses with different geometry, topology, and boundary conditions. |
著作権等: | © 2020 Hayashi and Ohsaki. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
URI: | http://hdl.handle.net/2433/259776 |
DOI(出版社版): | 10.3389/fbuil.2020.00059 |
出現コレクション: | 学術雑誌掲載論文等 |
このリポジトリに保管されているアイテムはすべて著作権により保護されています。