Studying the impact of aggregates and mix volumetric properties on the moisture resistance of asphalt concrete using a feed-Forward artificial neural network

Several studies have reported the effect of various additives on the moisture resistance of AC, but limited studies explored the impact of aggregate's properties on the moisture sensitivity of AC. In this study, the influence of aggregate properties and mix's volumetric properties on the m...

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Bibliographic Details
Published in:Road materials and pavement design 2023-11, Vol.24 (11), p.2737-2758
Main Authors: Dalhat, M. A., Osman, Sami A.
Format: Article
Language:English
Subjects:
Abrasion
Additives
Aggregates
artificial neural network
Artificial neural networks
asphalt concrete
Asphalt mixes
Clay minerals
Functions (mathematics)
moisture damage
Moisture effects
Moisture resistance
Neural networks
Polynomials
Profile method (forecasting)
Sensitivity
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Summary:Several studies have reported the effect of various additives on the moisture resistance of AC, but limited studies explored the impact of aggregate's properties on the moisture sensitivity of AC. In this study, the influence of aggregate properties and mix's volumetric properties on the moisture sensitivity of AC was studied. The moisture sensitivity of the AC was based on Retained Stability Index (RSI). The study utilised results from 319 plant-produced asphalt mixtures. The was modelled as a function of aggregates and mix's variables using Artificial Neural Network (ANN). The variables studied include air voids , void in mineral aggregates , clay lump , Los Angeles's abrasion , soundness value (SV), sand equivalence value , gradation and mix type. Profile method along with weight-connection relative importance ranking were employed to analyse the influence of the input variables on the . The relationship between these variables and the fits higher order polynomial functions.
ISSN:1468-0629
2164-7402
DOI:10.1080/14680629.2023.2165533