This study deals with estimation of fatigue lives of bituminous mixtures using artificial neural networks. Different types of fly ash were used as filler replacing agents in a dense bituminous mixture. Fatigue tests were performed using repeated load indirect tensile test apparatus under controlled stress conditions. For determination of fatigue life, the initiation of macro crack was accepted as the main criteria to terminate the test. The full-scale tests on asphalt pavement sections are very expensive and these tests require many years in order to be completed and sometimes do not end up with solid conclusions. Therefore, the determination of fatigue lives of bituminous mixtures in the laboratory environment is very important. This study used the experimental data as training set and, with proposed neural network architecture, very reasonable estimates of fatigue lives of bituminous mixtures have been obtained. The proposed approach provides real economy, time saving and allows observing the effect of fly ash replacement and composition on the mechanical properties of mixtures such as fatigue lives and their estimations without carrying out destructive tests.

A novel application of genetic programming (GP) for modelling and presenting closed form solutions to the rutting prediction for polypropylene (PP) modified asphalt mixtures is investigated. Various PP fibers have been utilised for bitumen modification and repeated creep (RC) tests have been carried out. Marshall specimens, fabricated with multifilament 3 mm (M-03) type PP fibers at optimum bitumen content of 5% have been tested under different load values and patterns at 50 °C to investigate their rutting potential. It has been shown that the service lives of PP fiber-reinforced Marshall specimens are respectively longer than the control specimens under the same testing conditions (5 to 12 times). Input variables in the developed GP model use the physical properties of Marshall specimens such as PP type, specimen height, unit weight, voids in mineral aggregate, voids filled with asphalt, air voids, rest period and pulse counts. The performance of the accuracy of the proposed GP model is observed to be quite satisfactory. To obtain the main effects plot, detailed parametric studies have been performed. The presened closed form solution will also help further researchers willing to perform studies on the prediction of the rutting potential of asphalt without carrying out destructive tests for similar type of aggregate sources, bitumen, aggregate gradation, modification technique and laboratory conditions.