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.

Soft computing methods for fatigue life estimation: A review of the current state and future trends

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“…Activation function Sigmoid Figueira Pujol and Andrade Pinto, 15 Susmikanti, 17 Rohman et al, 18 Han, 20 Aymerich and Serra, 21 Sohn and Bae, 24 Genel, 25 Junior et al, 26 Vassilopoulos et al, 28 Liao et al, 31 Al-Assadi et al, 32 Kumar et al, 33 Yang et al, 39 Uygur et al, 41 Martinez and Ponce, 47 Barbosa et al, 48 Srinivasan et al, 52 Park and Kang, 54 Vassilopoulos et al, 55 Majidian and Saidi, 56 Xiao et al, 59 Al-Assadi et al, 61 Jin et al, 67 Tapkin, 68 Moghaddam et al, 69 Durodola et al, 70 Durodola et al, 71 Yang et al 73 and Ahmad et al 82…”

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Development of a framework for standardisation of interlayer bond of asphalt pavements

“…Neural networks are non-linear statistical data modelling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data (Haykin, 1999, Tapkin, 2010.…”

“…Tapkin (Tapkin, 2010) The aim of another research (Xiao et al, 2010) was the application of ANN for predicting the penetration index of binders after long term aging. During the same study, ANN was compared to statistical regression models and it was found to predict the penetration index more accurately.…”