Rutting Prediction Model Developed by Genetic Programming Method Through Full Scale Accelerated Pavement Testing

The application of genetic programming (GP) to pavement performance evaluation is relatively new. This paper both describes and demonstrates how to develop a model to predict the pavement rutting by using GP method. Results from closely controlled full-scale Accelerated Pavement Testing (APT) - 7 test pavements (264 records) from CRRELpsilas HVS and 1 test pavement (8 records) from TxDOTpsilas MLS - were employed to establish a rutting prediction model. For model evaluation purposes, additional test pavements (94 records) from both CRRELpsilas HVS and TxDOTpsilas MLS were utilized. GP was applied successfully to develop a rutting prediction model that uses wheel load, load repetitions and the pavement Structural Number (SN) as inputs. The overall R² for 272 records is 0.8140. The model and algorithms proposed in this study provide a good foundation for further refinement when additional data is available.