| contributor author | Musharraf Zaman | |
| contributor author | Pranshoo Solanki | |
| contributor author | Ali Ebrahimi | |
| contributor author | Luther White | |
| date accessioned | 2017-05-08T21:32:09Z | |
| date available | 2017-05-08T21:32:09Z | |
| date copyright | February 2010 | |
| date issued | 2010 | |
| identifier other | %28asce%291532-3641%282010%2910%3A1%281%29.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/55213 | |
| description abstract | Artificial neural network (ANN) models are developed in this study to correlate resilient modulus with routine properties of subgrade soils and state of stress for pavement design application. A database is developed containing grain size distribution, Atterberg limits, standard Proctor, unconfined compression, and resilient modulus results for 97 soils from 16 different counties in Oklahoma. Of these, 63 soils ( | |
| publisher | American Society of Civil Engineers | |
| title | Neural Network Modeling of Resilient Modulus Using Routine Subgrade Soil Properties | |
| type | Journal Paper | |
| journal volume | 10 | |
| journal issue | 1 | |
| journal title | International Journal of Geomechanics | |
| identifier doi | 10.1061/(ASCE)1532-3641(2010)10:1(1) | |
| tree | International Journal of Geomechanics:;2010:;Volume ( 010 ):;issue: 001 | |
| contenttype | Fulltext | |
