آمار
| تعداد نشریات | 197 |
| تعداد شمارهها | 4,923 |
| تعداد مقالات | 53,925 |
| تعداد مشاهده مقاله | 89,987,231 |
| تعداد دریافت فایل اصل مقاله | 72,796,536 |
Performance Evaluation of RBF Networks with Various Variables to Forecast the Properties of SCCs | ||
| Civil Engineering Infrastructures Journal | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 08 دی 1399 | ||
| نوع مقاله: Research Papers | ||
| شناسه دیجیتال (DOI): 10.22059/ceij.2020.288257.1611 | ||
| نویسندگان | ||
Atefeh Gholamzadeh Chitgar  1؛ Javad Berenjian2
| ||
| 1Department of Civil Engineering, Tabari University of Babol, Babol, Iran | ||
| 2Faculty of Civil Engineering, Babol University of Technology, Babol – Iran | ||
| چکیده | ||
| In the present study, Radial Basis Function (RBF) neural networks were applied to forecast the compressive strength and elastic modulus of Self-Compacting Concrete (SCC). To construct the models, different experimental specimens of diverse kinds of SCC were gathered from the literature. The data used in the networks were classified into two different sets of input parameters. The results revealed that the proposed RBF models can accurately forecast the properties of SCCs with low test error. Furthermore, a comparison between models with two different sets of inputs proves that the selected parameters as input variables, straightly impress the precision of the networks, in the prediction of the intended outputs. | ||
| کلیدواژهها | ||
| Parameters؛ RBF Artificial Neural Networks؛ Self-Compacting Concrete؛ Test MSE | ||
|
آمار تعداد مشاهده مقاله: 1,480 |
||
