Modelling the Effects of Hydrated Lime Additives on Asphalt Mixtures by Fuzzy Logic and ANN

Abstract

In this study, Marshall test results of hot mix asphalt samples having various Hydrated Lime (HL) content rates were modelled using Fuzzy Logic (FL) and Artificial Neural Networks (ANN). Test sets having various HL content were prepared in order to investigate the effect of HL. Marshall Stability test was performed on the samples to obtain the optimal Asphalt Content (AC) ratio. The results were evaluated in order to determine HL additives’ sensitivity on the mixture parameters. The Marshall Test results were used to develop the FL and ANN models. The models developed produced acceptable estimations of the mixture parameters.

Keywords

• Hot mix asphalt,Hydrated lime,Marshall mix design,Fuzzy Logic,Artificial Neural Networks

Modelling the Effects of Hydrated Lime Additives on Asphalt Mixtures by Fuzzy Logic and ANN

Abstract

In this study, Marshall test results of hot mix asphalt samples having various Hydrated Lime (HL) content rates were modelled using Fuzzy Logic (FL) and Artificial Neural Networks (ANN). Test sets having various HL content were prepared in order to investigate the effect of HL. Marshall Stability test was performed on the samples to obtain the optimal Asphalt Content (AC) ratio. The results were evaluated in order to determine HL additives’ sensitivity on the mixture parameters. The Marshall Test results were used to develop the FL and ANN models. The models developed produced acceptable estimations of the mixture parameters.

Keywords

• Hot mix asphalt,hydrated lime,marshall mix design,fuzzy logic,artificial neural networks

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