| dc.creator |
Saltan, M |
|
| dc.creator |
Tigdemir, M |
|
| dc.creator |
Karasahin, M |
|
| dc.date |
2000-01-01T01:00:00Z |
|
| dc.date.accessioned |
2021-12-03T11:54:16Z |
|
| dc.date.available |
2021-12-03T11:54:16Z |
|
| dc.identifier |
ba166799-3648-4ddf-8bee-bddc15fa6276 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/ba166799-3648-4ddf-8bee-bddc15fa6276/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/94524 |
|
| dc.description |
The deformation of granular material under traffic loading is composed of two parts, resilient and permanent. The resilient response of the pavement becomes more important during service. However, for a pavement foundation the only criterion is the plastic deformation. After constructing the upper layers the plastic strain much smaller than the increment of resilient deformation. In the study resilient test results obtained from repeated load triaxial apparatus were modelled using artificial neural network approach. Some experimental data firstly were trained, after that other results which were not used in the training were used to predict the other stress paths. Regression analysis was then carried out between experimental results and predicted values. High values of regression coefficients were obtained. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Artificial neural network modelling of granular material behaviour under repeated loading |
|
| dc.type |
info:eu-repo/semantics/conferenceObject |
|