| dc.creator |
TERZİ, Serdal |
|
| dc.creator |
SALTAN, Mehmet |
|
| dc.date |
2008-06-30T21:00:00Z |
|
| dc.date.accessioned |
2020-10-06T11:37:47Z |
|
| dc.date.available |
2020-10-06T11:37:47Z |
|
| dc.identifier |
e51bdbfa-b232-4283-95f2-87fdc26d092d |
|
| dc.identifier |
10.1016/j.advengsoft.2007.06.002 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/e51bdbfa-b232-4283-95f2-87fdc26d092d/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/74659 |
|
| dc.description |
Through the new technological developments, for highway maintenance engineering the structural capacity of pavement is to be determined using non-destructive techniques. Up to now various methodologies have been applied based on the surface deflection bowl obtained under either a known moving wheel load or devices such as falling weight deflectometer. Backcalculating pavement layer moduli are well-accepted procedures in the evaluation of the structural capacity of pavements. The ultimate aim of the backcalculation process from non-destructive testing (NDT) results is to estimate the pavement material properties. Using backcalculation analysis, in situ material properties can be backcalculated by the measured field data for appropriate analysis techniques. To backcalculate reliable moduli, the deflection basin must be modeled more realistically. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Modeling deflection basin using artificial neural networks with cross-validation technique in backcalculating flexible pavement layer moduli |
|
| dc.type |
info:eu-repo/semantics/article |
|