Prediction of Binder Content in Glass Fiber Reinforced Asphalt Mix Using Machine Learning Techniques

Abstract

Several researchers have been reported the results of adding a variety of fibers to asphalt concrete described as fiber-reinforced asphalt concrete FRAC. This research paper finds the most suitable prediction model for Marshall Stability and the optimistic bitumen content useful in glass fiber reinforced asphalt mix by performing Marshall Stability tests and further analyzing the data in consonance with published research. Four machine learning approaches were used to find the best prediction model: Artificial Neural Network, Support-Vector-Machine, Gaussian-Process, and Random-Forest. Seven statistical metrics were used to evaluate the performance of the applied models i.e., Coefficient-of-correlation (CC), Mean-Absolute-Error (MAE), Root mean squared error (RMSE), Relative absolute error (RAE), Root relative squared error (RRSE), Scattering index (SI), and (BIAS). Test results of testing stage indicated that the Support Vector Machine (SVM_PUK) model performs the best in validation amongst all applied models with CC values as 0.8776 MAE as 1.2294, RMSE as 1.9653, RAE as 38.33%, RRSE as 55.22%, SI as 1.0648 and BIAS as 0.5005. The Taylor diagram of the testing dataset also confirms that the model based on SVM outperforms the other models. Results of sensitivity analysis show that the bitumen content of about 5% has a significant effect on the Marshall Stability.