WEKO3
アイテム
Performance of artificial neural network system in prediction issues of earthquake engineering
http://hdl.handle.net/10458/6589
http://hdl.handle.net/10458/6589022e0c77-0234-40c1-84ab-5ad7b928be65
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
本文 (543.0 kB)
|
|
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2020-06-21 | |||||
| タイトル | ||||||
| タイトル | Performance of artificial neural network system in prediction issues of earthquake engineering | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ | journal article | |||||
| 著者 |
Emami, S.M.R.
× Emami, S.M.R.× Iwao, Yushiro× 原田, 隆典 |
|||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Prediction is one of the most important issues of earthquake engineering. Empirical predictive relations are commonly played as basic rule in seismic hazard analysis. Such relations are generally expressed as mathematical functions connecting a strong motion parameter to the parameters characterising the earthquake source, the propagation path distance and the local site conditions. Regression analysis has been widely used among other analytical methods with different techniques during the past few decades, e.g. Gutenberg & Richter （1956）, McGuire （1978）, Joyner & Boore （1981）, and Molas & Yamazaki （1995）, etc.. Artificial neural networks were first applied to prediction issues of earthquake engineering by Emami et al. （1996）. The performance of this advanced system in various aspects of prediction of ground motion parameters is discussed and compared with traditional procedures throughout this paper. | |||||
| 書誌情報 |
8th Congress of the International Association for Engineering Geology and the Enviroment 巻 2, 発行日 1998-09 |
|||||
| 著者版フラグ | ||||||
| 出版タイプ | VoR | |||||
