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Experimental and Machine Learning Approach to Investigate the Mechanical Performance of Asphalt Mixtures with Silica Fume Filler

Tiwari N.
•
Rondinella F.
•
Satyam N.
•
Baldo N.
2023
  • journal article

Periodico
APPLIED SCIENCES
Abstract
This study explores the potential in substituting ordinary Portland cement (OPC) with industrial waste silica fume (SF) as a mineral filler in asphalt mixtures (AM) for flexible road pavements. The Marshall and indirect tensile strength tests were used to evaluate the mechanical resistance and durability of the AMs for different SF and OPC ratios. To develop predictive models of the key mechanical and volumetric parameters, the experimental data were analyzed using artificial neural networks (ANN) with three different activation functions and leave-one-out cross-validation as a resampling method. The addition of SF resulted in a performance comparable to, or slightly better than, OPC-based mixtures, with a maximum indirect tensile strength of 1044.45 kPa at 5% bitumen content. The ANN modeling was highly successful, partly due to an interpolation-based data augmentation strategy, with a correlation coefficient RCV of 0.9988.
DOI
10.3390/app13116664
Archivio
https://hdl.handle.net/11390/1252146
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85161600258
https://ricerca.unityfvg.it/handle/11390/1252146
Diritti
open access
Soggetti
  • artificial neural net...

  • asphalt mixture

  • cement

  • data augmentation

  • recycling

  • silica fume

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