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<p>Expansive soils possess characteristics of change in volume considering variation in moisture. These traits make them very challenging in the integration of pavements. This research aims to enhance the strength of expansive soil through waste materials and proposes a novel Optimized Octopus Recurrent Nets (OORN) for strength prediction of expansive soil from the percentage of waste materials. The waste material considered in the research includes sugarcane bagasse ash (SBA) in 0%, 5%, 10%, 15%, 20%, and 25% of the weight of soil. Initially, the modified proctor compaction (MPC) test was carried out to determine the optimum moisture content and maximum dry density. Then, the California bearing ratio (CBR) test and the unconfined compressive strength (UCS) test are used to determine the optimum percentage of SBA. The optimum percentage of SBA in expansive soil was 15%. Afterwards, calcium lignosulfonate (CLS) is added to the optimum mixture in 1%, 2%, 3% and 4% of the weight of soil. In which the mix with 15% SBA and 3% CLS has attained maximum strength values in CBR and UCS tests. The novel OORN prediction model was developed from the experimental data for predicting the CBR and UCS of the expansive soil, considering the percentages of SBA and CLS. In the end, the experimental and the predicted values are compared to conclude the research.</p>

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A hybrid model for predicting the subgrade improvement using waste materials (sugarcane bagasse ash & calcium lignosulfonate)

  • 期刊论文

  • 发表日期：

    2026年4月1日

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  • Priyanka Mishra,
  • Shalinee Shukla,
  • Kumar Venkatesh

摘要

Expansive soils possess characteristics of change in volume considering variation in moisture. These traits make them very challenging in the integration of pavements. This research aims to enhance the strength of expansive soil through waste materials and proposes a novel Optimized Octopus Recurrent Nets (OORN) for strength prediction of expansive soil from the percentage of waste materials. The waste material considered in the research includes sugarcane bagasse ash (SBA) in 0%, 5%, 10%, 15%, 20%, and 25% of the weight of soil. Initially, the modified proctor compaction (MPC) test was carried out to determine the optimum moisture content and maximum dry density. Then, the California bearing ratio (CBR) test and the unconfined compressive strength (UCS) test are used to determine the optimum percentage of SBA. The optimum percentage of SBA in expansive soil was 15%. Afterwards, calcium lignosulfonate (CLS) is added to the optimum mixture in 1%, 2%, 3% and 4% of the weight of soil. In which the mix with 15% SBA and 3% CLS has attained maximum strength values in CBR and UCS tests. The novel OORN prediction model was developed from the experimental data for predicting the CBR and UCS of the expansive soil, considering the percentages of SBA and CLS. In the end, the experimental and the predicted values are compared to conclude the research.