ダウンロード数: 129
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 | |
---|---|---|---|---|
a58b0p49.pdf | 295.97 kB | Adobe PDF | 見る/開く |
完全メタデータレコード
DCフィールド | 値 | 言語 |
---|---|---|
dc.contributor.author | 浜口, 俊雄 | ja |
dc.contributor.author | 角, 哲也 | ja |
dc.contributor.author | 田中, 茂信 | ja |
dc.contributor.alternative | HAMAGUCHI, Toshio | en |
dc.contributor.alternative | SUMI, Tetsuya | en |
dc.contributor.alternative | TANAKA, Shigenobu | en |
dc.date.accessioned | 2016-04-07T02:40:28Z | - |
dc.date.available | 2016-04-07T02:40:28Z | - |
dc.date.issued | 2015-06 | - |
dc.identifier.issn | 0386-412X | - |
dc.identifier.uri | http://hdl.handle.net/2433/210049 | - |
dc.description.abstract | This paper demonstrates the performance of basic triangle designing of gravity dam using the artificial swarm intelligence approaches such as PSO, ACO and GA. The obstacle to determine a basic triangle is to optimize the parameters to be designed as minimize the triangle area because the shape of the fillet makes the vertical forces of the static water and sediment pressures with bilinear processes in cases that a sediment height gets greater than a fillet one. In this research, two approaches are carried out. The first one is to employ the 0-extension approach to express the bilinear situation as one equation. The second one is to use the artificial swarm intelligence approach to optimize the dam parameters in bilinear processes. A given sedimentation height is reconsidered to know the upper limitation in managing the long-term dam sedimentation. It can be concluded that this research is useful and helpful in overcoming the difficulty of optimally designing the bilinear basic-triangle parameters and in managing the long-term sedimentation. | en |
dc.format.mimetype | application/pdf | - |
dc.language.iso | jpn | - |
dc.publisher | 京都大学防災研究所 | ja |
dc.publisher.alternative | Disaster Prevention Research Institute, Kyoto University | en |
dc.subject | 重力ダム | ja |
dc.subject | 基本断面 | ja |
dc.subject | 人工群知能 | ja |
dc.subject | 拡張 | ja |
dc.subject | 堆砂容量 | ja |
dc.subject | Gravity dam | en |
dc.subject | Basic section | en |
dc.subject | Artificial swarm intelligence | en |
dc.subject | 0-extension | en |
dc.subject | Capacity reservoir sedimentation | en |
dc.subject.ndc | 519.9 | - |
dc.title | 人工群知能を用いた重力ダム設計基本断面の最適化と長期ダム安定性管理への応用 | ja |
dc.title.alternative | Design Optimization of Basic Section for Gravity Dam Using Artificial Swarm Intelligence and Its Application to Long-term Management of Dam Stability | en |
dc.type | departmental bulletin paper | - |
dc.type.niitype | Departmental Bulletin Paper | - |
dc.identifier.ncid | AN00027784 | - |
dc.identifier.jtitle | 京都大学防災研究所年報. B | ja |
dc.identifier.volume | 58 | - |
dc.identifier.issue | B | - |
dc.identifier.spage | 433 | - |
dc.identifier.epage | 440 | - |
dc.textversion | publisher | - |
dc.sortkey | 49 | - |
dc.relation.url | http://www.dpri.kyoto-u.ac.jp/nenpo/nenpo.html | - |
dcterms.accessRights | open access | - |
dc.identifier.pissn | 0386-412X | - |
dc.identifier.jtitle-alternative | Disaster Prevention Research Institute Annuals. B | en |
出現コレクション: | Vol.58 B |
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。