Studying the impact of aggregates and mix volumetric properties on the moisture resistance of asphalt concrete using a feed-Forward artificial neural network
Source abstract
Abstract
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 RSI was modelled as a function of aggregates and mix’s variables using Artificial Neural Network (ANN). The variables studied include air voids (AV), void in mineral aggregates (VMA), clay lump (CL), Los Angeles’s abrasion (LA), soundness value (SV), sand equivalence value (SEV), 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 RSI. The relationship between these variables and the RSI fits higher order polynomial functions.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Asphalt Pavement Performance Evaluation · Infrastructure Maintenance and Monitoring · Smart Materials for Construction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
References (50)
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