| contributor author | Kasthurirangan Gopalakrishnan | |
| contributor author | Sunghwan Kim | |
| date accessioned | 2017-05-08T21:43:26Z | |
| date available | 2017-05-08T21:43:26Z | |
| date copyright | February 2011 | |
| date issued | 2011 | |
| identifier other | %28asce%29em%2E1943-7889%2E0000223.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/60672 | |
| description abstract | The application of artificial intelligence (AI) techniques to engineering has increased tremendously over the last decade. Support vector machine (SVM) is one efficient AI technique based on statistical learning theory. This paper explores the SVM approach to model the mechanical behavior of hot-mix asphalt (HMA) owing to high degree of complexity and uncertainty inherent in HMA modeling. The dynamic modulus | |
| publisher | American Society of Civil Engineers | |
| title | Support Vector Machines Approach to HMA Stiffness Prediction | |
| type | Journal Paper | |
| journal volume | 137 | |
| journal issue | 2 | |
| journal title | Journal of Engineering Mechanics | |
| identifier doi | 10.1061/(ASCE)EM.1943-7889.0000214 | |
| tree | Journal of Engineering Mechanics:;2011:;Volume ( 137 ):;issue: 002 | |
| contenttype | Fulltext | |
