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 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 (Formula presented.) was modelled as a function of aggregates and mix’s variables using Artificial Neural Network (ANN). The variables studied include air voids (Formula presented.), void in mineral aggregates (Formula presented.), clay lump (Formula presented.), Los Angeles’s abrasion (Formula presented.), soundness value (SV), sand equivalence value (Formula presented.), 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 (Formula presented.). The relationship between these variables and the (Formula presented.) fits higher order polynomial functions.