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

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Chang:2008:ICNC,
  author =       "Jia-Ruey Chang and Shun-Hsing Chen and 
                 Dar-Hao Chen and Yao-Bin Liu",
  title =        "Rutting Prediction Model Developed by Genetic
                 Programming Method Through Full Scale Accelerated
                 Pavement Testing",
  booktitle =    "Fourth International Conference on Natural
                 Computation, ICNC '08",
  year =         "2008",
  month =        oct,
  volume =       "6",
  pages =        "326--330",
  keywords =     "genetic algorithms, genetic programming, accelerated
                 pavement testing, load repetitions, model evaluation,
                 pavement performance evaluation, pavement rutting,
                 pavement structural number, rutting prediction model,
                 test pavements, wheel load, structural engineering
                 computing",
  DOI =          "doi:10.1109/ICNC.2008.673",
  abstract =     "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 CRREL's HVS and 1 test pavement (8
                 records) from TxDOT's MLS - were employed to establish
                 a rutting prediction model. For model evaluation
                 purposes, additional test pavements (94 records) from
                 both CRREL's HVS and TxDOT's MLS were used. 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.",
  notes =        "Discipulus Also known as \cite{4667854}",
}

Genetic Programming entries for Jia-Ruey Chang Shun-Hsing Chen Dar-Hao Chen Yao-Bin Liu

Citations