Prediction of Marshall Test Results for Dense Glasphalt Mixtures Using Artificial Neural Networks
نویسندگان
چکیده
Asphalt mixture comprising waste glass as an aggregate is referred to “ glasphalt” . Limited studies have been oriented investigate the Marshall test results of dense-graded glasphalt mixes considering a wide range variables. This study aims utilize artificial neural networks (ANNs) develop predictive models for stability and flow dense based on large experimental database collected from literature. Eight independent variables covering material mix properties were utilized inputs in models. The proposed resulted experimental-to-predicted ratio 1.00 1.00, coefficient variation 8.6% 8.7%, RMSE 1.63 kN 0.54 mm, R-squared 93.6% 85.7% models, respectively. Comprehensive parametric analyses conducted further validate by investigating sensitivity their parameters predicted values. revealed some desirable design values that could be considered better performance mixes. indicate 4% desired air void content High value can achieved containing crushed 12.5 mm maximum size 50% cullet 4.75 size. Lower viscosity asphalt binder would provide uniformly compacted Furthermore, increases ingredient particles, penetration grade cement, cement content, VMA% increase.
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ژورنال
عنوان ژورنال: Frontiers in Built Environment
سال: 2022
ISSN: ['2297-3362']
DOI: https://doi.org/10.3389/fbuil.2022.949167