| dc.creator |
Küçükçapraz, Burak |
|
| dc.creator |
Terzi, Serdal |
|
| dc.date |
2021-12-01T00:00:00Z |
|
| dc.date.accessioned |
2022-05-10T11:17:26Z |
|
| dc.date.available |
2022-05-10T11:17:26Z |
|
| dc.identifier |
45fda3f4-bf8c-4832-972c-afb98b132d81 |
|
| dc.identifier |
10.53635/jit.1015881 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/45fda3f4-bf8c-4832-972c-afb98b132d81/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/96592 |
|
| dc.description |
<p><span style="color: rgb(17, 17, 17); font-family: Poppins; font-size: 14px;">Rigid pavements slab thicknesses are determined using readings from design curves where human, reading, and curve mistakes could commonly occur. In addition, readings from these design curves take precious time and need high attention and diligence. In this study, the ANFIS model is developed instead of the traditional curve reading method, which is more practical and timesaving. So, it could decrease the mistakes which are occurring from curve readings. For this purpose, it has produced a random data set. A slab thickness for each data in the set has been determined using design curve readings. Obtained slab thicknesses are used for training the ANFIS model and an alternative method has been obtained. The created model has predicted the slab thicknesses with a regression of 97.05% compared to the slab thicknesses obtained from curve readings.</span><br></p> |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Planning of airport pavement with artificial intelligence methods |
|
| dc.type |
info:eu-repo/semantics/article |
|