| 種別 | 論文 |
| 主題 | STRESS-STRAIN MODELING OF CONFINED CONCRETE USING ARTIFICIAL NEURAL NETWORKS |
| 副題 | |
| 筆頭著者 | Mingyang ZHANG（Waseda University） |
| 連名者1 | Weilun WANG（Shenzhen University） |
| 連名者2 | 秋山充良（早稲田大学） |
| 連名者3 | |
| 連名者4 | |
| 連名者5 | |
| キーワード | artificial neural networks、compressive fracture energy、confined concrete、gauge length |
| 巻 | 40 |
| 号 | 2 |
| 先頭ページ | 121 |
| 末尾ページ | 126 |
| 年度 | 2018 |
| 要旨 | A stress-strain model was proposed based on the artificial neural networks (ANN) to predict the behavior of confined concrete columns under concentric compression. A wide range of previous experimental data including 182 samples were collected for establishing ANN model. Gauge length in the compressive test was used in the input layer of ANN model to take into consideration the difference of compressive fracture energy. The proposed stress-strain model provides good agreement with the test results independent of the compressive strength of concrete, yield strength of tie, and gauge length. |
| PDFファイル名 | 040-01-2021.pdf |