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<p>Using traditional materials, especially earthen materials and plant waste, is becoming more popular worldwide due to lower construction costs, improved thermal comfort, reduced energy use, and fewer carbon dioxide emissions. In this context, tests have been performed on a stabilized earth bricks (SEBs) made from raw earth bricks combined with <i>Vicia faba</i> waste (<i>Vf</i>W) and biochar derived from this waste (BVfW), obtained by pyrolysis at 300&#xa0;°C and 500&#xa0;°C, using a factorial design with B<i>Vf</i>W contents ranging from 0.5 to 6 wt%. The effects of these compounds on the controlled variables were examined using artificial neural network (ANN) techniques and response surface methodology (RSM) to assess the mechanical behavior and thermophysical properties of the bricks within a system that varies the percentage of B<i>Vf</i>W and pyrolysis temperature. The best conditions were found to be 500&#xa0;°C and 4% B<i>Vf</i>W content, based on the RSM desirability function and the ANN genetic algorithm. This experimental approach allows for optimizing production conditions by selecting the best parameters to create bricks with balanced strength.</p>

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Optimizing the mechanical performance of adobe bricks reinforced with Vicia faba plant waste and derived biochar using ANN and RSM

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月18日

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  • Nadia Frioui,
  • Messaouda Boumaaza,
  • Ahmed Belaadi,
  • Mahmood M. S. Abdullah,
  • Djamel Ghernaout,
  • Amar Al-Khawlani,
  • Herbert Mukalazi

摘要

Using traditional materials, especially earthen materials and plant waste, is becoming more popular worldwide due to lower construction costs, improved thermal comfort, reduced energy use, and fewer carbon dioxide emissions. In this context, tests have been performed on a stabilized earth bricks (SEBs) made from raw earth bricks combined with Vicia faba waste (VfW) and biochar derived from this waste (BVfW), obtained by pyrolysis at 300 °C and 500 °C, using a factorial design with BVfW contents ranging from 0.5 to 6 wt%. The effects of these compounds on the controlled variables were examined using artificial neural network (ANN) techniques and response surface methodology (RSM) to assess the mechanical behavior and thermophysical properties of the bricks within a system that varies the percentage of BVfW and pyrolysis temperature. The best conditions were found to be 500 °C and 4% BVfW content, based on the RSM desirability function and the ANN genetic algorithm. This experimental approach allows for optimizing production conditions by selecting the best parameters to create bricks with balanced strength.