| dc.creator |
KILINÇARSLAN, Şemsettin |
|
| dc.creator |
Ceylan, Hakan |
|
| dc.creator |
Davraz, Metin |
|
| dc.date |
2015-07-31T21:00:00Z |
|
| dc.date.accessioned |
2020-10-06T12:03:17Z |
|
| dc.date.available |
2020-10-06T12:03:17Z |
|
| dc.identifier |
fb6ebab2-b24e-4fe5-9e33-6eb9a3a0658d |
|
| dc.identifier |
10.12693/aphyspola.128.b-184 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/fb6ebab2-b24e-4fe5-9e33-6eb9a3a0658d/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/76898 |
|
| dc.description |
Artificial neural network is generally information processing system and a computer program that imitates human brain neural network system. By entering the information from outside, artificial neural network can be trained on examples related to a problem, so that modeling of the problem is provided. In this study, compressive strength, Poisson ratio of the lightweight concrete specimens, which have different natural lightweight aggregates, were modeled with artificial neural network. The data which were provided by artificial neural network model were compared with the data obtained from experimental study and a good agreement was determined between the results. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Predicting the Poisson Ratio of Lightweight Concretes using Artificial Neural Network |
|
| dc.type |
info:eu-repo/semantics/article |
|