Predicting the laboratory rutting response of asphalt mixtures using different neural network algorithms

Abstract

ABSTRACT The permanent deformation of asphalt pavement under similar traffic conditions depends on a number of factors. The factors considered in this study are bitumen source, aggregate source, aggregate gradation, bulk specific gravity of aggregates (Gsb), percentage of aggregates passing #4 sieve, air voids (Va), optimum bitumen content, binder grade, load repetitions, temperature, and Marshall stability. Asphalt pavement analyzer (APA) test, Cooper wheel tracking test (CWTT), and repeated load axial test (RLAT) were performed on thirteen different types of hot mixed asphalt (HMA) mixtures. Three artificial neural network (ANN) algorithms, namely Backpropagation (BP), Conjugate gradient (CG), and Broyden-Fletcher Goldfarb-Shanno (BFGS) were used to analyse the data. The best fit ANN algorithm for each of the laboratory tests (APA, CWTT, RLAT) was selected, based on the coefficient of determination (R-squared), root-mean-square error (RMSE), mean bias error (MBE) and the mean square error (MSE) closest to the gamma statistic Г. The results showed no single ANN algorithm is suitable for predicting all HMA rutting susceptibility tests data. The BP algorithm most appropriately predicts APA test data, the BFGS algorithm precisely fits CWTT results, and the CG algorithm seems most suitable to predict RLAT data. However, further, differentiating testing is required for a more precise comparison of rutting predicting ability of various ANN algorithms.