<p>The increasing need for efficient and sustainable construction materials has prompted the investigation of local and recycled resources to improve the characteristics of poor soils. This research aims to enhance dune sand (DS)—a plentiful yet geotechnically weak material—by integrating rubber crumb (RC) and brick powder (BP), with the objectives of soil stability and waste valorization. Experimental formulations were created with RC at concentrations of 10%, 30%, and 50%, and BP at concentrations of 1%, 2%, and 3%. A response surface methodology (RSM) and artificial neural network (ANN) techniques were utilized to examine the influences of RC and BP content on three primary responses: maximum dry density (MDD), internal friction angle (ϕ), and cohesion (C). Ideal conditions with 40.7% RC and 3% BP were achieved through optimization utilizing RSM and ANN-GA, greatly enhancing compaction and shear strength for geotechnical applications. According to Life Cycle Assessment (LCA), a high CR content raises energy consumption (E-Energy) and greenhouse gas emissions (E-CO₂), primarily as a result of rubber recycling. However, the reduction in sand mining and the diversion of tyre waste balance these effects. Therefore, the combination of RC with BP is turning out to be a viable and efficient infrastructure solution, especially in dry regions.</p>

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Geotechnical improvement of dune sand embankments using rubber crumb and brick powder: RSM vs. ANN-GA hybrid optimization

  • Fatima Zohra Benamara,
  • Chiraz Kechkar,
  • Messaouda Boumaaza,
  • Ahmed Belaadi,
  • Mohamed Sahraoui,
  • Mostefa Hani,
  • Ibrahim M. H. Alshaikh,
  • Sarra Mezaouri,
  • Lina Zaidi,
  • Djamel Ghernaout,
  • Yazid Chetbani

摘要

The increasing need for efficient and sustainable construction materials has prompted the investigation of local and recycled resources to improve the characteristics of poor soils. This research aims to enhance dune sand (DS)—a plentiful yet geotechnically weak material—by integrating rubber crumb (RC) and brick powder (BP), with the objectives of soil stability and waste valorization. Experimental formulations were created with RC at concentrations of 10%, 30%, and 50%, and BP at concentrations of 1%, 2%, and 3%. A response surface methodology (RSM) and artificial neural network (ANN) techniques were utilized to examine the influences of RC and BP content on three primary responses: maximum dry density (MDD), internal friction angle (ϕ), and cohesion (C). Ideal conditions with 40.7% RC and 3% BP were achieved through optimization utilizing RSM and ANN-GA, greatly enhancing compaction and shear strength for geotechnical applications. According to Life Cycle Assessment (LCA), a high CR content raises energy consumption (E-Energy) and greenhouse gas emissions (E-CO₂), primarily as a result of rubber recycling. However, the reduction in sand mining and the diversion of tyre waste balance these effects. Therefore, the combination of RC with BP is turning out to be a viable and efficient infrastructure solution, especially in dry regions.