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<p>This study explores the development of an affordable and sustainable composite mix using locally available soil (LS), bentonite (B), and fly ash (FA) for municipal solid waste landfill liners. Experimental analysis revealed an optimal 65:15:20 LS-B-FA mix, significantly enhancing geotechnical properties. The mix improved the liquid limit (48.57%), plastic limit (32.33%), and hydraulic conductivity (96.04% decrease), whereas the unconfined compressive strength (UCS) increased by 209% after 28 d. Traditional UCS evaluation methods are labor intensive, prompting the application of multivariate adaptive regression splines and minimax probability machine regression for predictive modeling. These models demonstrated high accuracy, offering a reliable alternative for rapid mix evaluation. The integration of machine learning and experimental methods enhances design efficiency, supporting cost-effective landfill liner development. The optimal FA content further improves sustainability, reducing industrial waste while enhancing mechanical performance, making the proposed mix a viable solution for MSW landfill containment.</p>

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Development of an affordable and eco-friendly composite liner for municipal solid waste landfills using locally available materials and industrial waste: A hybrid experimental–machine learning study

  • 期刊论文

  • 发表日期：

    2026年1月1日

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  • Rajiv Kumar,
  • Divesh Ranjan Kumar,
  • Sunita Kumari,
  • Warit Wipulanusat

摘要

This study explores the development of an affordable and sustainable composite mix using locally available soil (LS), bentonite (B), and fly ash (FA) for municipal solid waste landfill liners. Experimental analysis revealed an optimal 65:15:20 LS-B-FA mix, significantly enhancing geotechnical properties. The mix improved the liquid limit (48.57%), plastic limit (32.33%), and hydraulic conductivity (96.04% decrease), whereas the unconfined compressive strength (UCS) increased by 209% after 28 d. Traditional UCS evaluation methods are labor intensive, prompting the application of multivariate adaptive regression splines and minimax probability machine regression for predictive modeling. These models demonstrated high accuracy, offering a reliable alternative for rapid mix evaluation. The integration of machine learning and experimental methods enhances design efficiency, supporting cost-effective landfill liner development. The optimal FA content further improves sustainability, reducing industrial waste while enhancing mechanical performance, making the proposed mix a viable solution for MSW landfill containment.