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

Abstract

In this study, Marshall design test parameters of hot mix asphalt samples with various rates of Hydrated Lime (HL) content were modelled using Fuzzy Logic (FL) and Artificial Neural Networks (ANN). HL was used as an additive material in asphalt mixtures and it affects the properties of the mixture. The effect of this material varies depending on the rate of use and the asphalt content of the mixtures. With the Marshall Stability test, optimal Asphalt Content (AC) ratios in the mixtures were obtained. The effect of the HL additive, which was introduced precisely in the mixtures in a wide range, on the Marshall parameters and depending also on the asphalt content was investigated. For this purpose, 15 Marshall design sets were prepared by decreasing the ratio of the mineral filler in the mixture starting from 6.8% by weight, by 0.5% intervals, and replacing it with the same ratio of HL. In addition, 45 control samples were produced for soft-computation. Marshall test results showed that the use of HL additive with lower amounts in the mixtures yields better results compared to higher rates in terms of the material properties. The Marshall test results were used to develop the FL and ANN models. The models which were developed produced acceptable estimations of the mixture parameters.