Artificial neural networks for predicting low temperature performances of modified asphalt mixtures

Tasdemir Y.

INDIAN JOURNAL OF ENGINEERING AND MATERIALS SCIENCES, vol.16, no.4, pp.237-244, 2009 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 16 Issue: 4
  • Publication Date: 2009
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.237-244
  • Keywords: Artificial neural network, General linear model, Thermal stress restrained specimen test, Fracture temperature, Fracture strength, COMMERCIAL WAXES, FEEDFORWARD NETWORKS, POLYPHOSPHORIC ACID, FLOW PREDICTION, POLYMER, ALGORITHM, MODEL, MODULUS, BITUMEN
  • Yozgat Bozok University Affiliated: Yes


In this study, the estimation of the low temperature performance of modified asphalt mixtures is investigated by using multi-layer perceptrons (MLP) which is one of the artificial neural networks (ANNs) techniques and general linear model (GLM). The fastest MLP training algorithm, that is the Levenberg-Marquardt algorithm, is used for optimization of the network weights. The ANN test results are compared to GLM results. GLM has, historically, been used to model the low temperature performance (fracture temperature and fracture strength) of asphalt pavements. The data used in the ANN model and GLM are arranged in a format of four input parameters that cover additive type, asphalt binder content, aging level and air void content, and output parameters which are the fracture temperature and the fracture strength. Based on the comparisons, it is found that the ANN generally gives better fracture temperature and fracture strength estimates than the GLM technique.