Estimation of fatigue lives of fly ash modified dense bituminous mixtures based on artificial neural networks

Abstract

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.

Keywords

• fatigue life estimation
• fly ash
• universal testing machine
• dense bituminous mixtures
• repeated load indirect tensile test
• neural networks