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<p>This study investigates the potential of utilizing textile sludge and glass powder as sustainable materials in the production of environmentally friendly concrete paving blocks. The research aimed to evaluate the feasibility of incorporating varying percentages of textile sludge (0%, 10%, 20%, 30%, and 40% by weight of fine aggregate) and glass powder (0%, 5%, 10%, 15%, and 20% by weight of cement) into the concrete mix, analyzing its effects on the mechanical properties, workability, and durability of the paving blocks. The prepared mix samples were subjected to comprehensive testing, including compressive strength, flexural strength, tensile splitting strength, water absorption, and abrasion resistance, conforming to relevant standards. Results indicated that the addition of textile sludge and glass powder improved the workability of the concrete mix, with optimal mechanical performance achieved at a composite mix of 20% textile sludge and 15% glass powder. This formulation resulted in a compressive strength of 44.34&#xa0;MPa after 28 days, meeting the standards for high-performance paving blocks. To enhance predictive capability and optimize mix design, a machine learning-based performance model was developed using the XGBoost regression algorithm. The model demonstrated high predictive accuracy (R² &gt;0.95) across multiple target variables, including compressive, flexural, and tensile strength, as well as water absorption. This data-driven approach enabled rapid performance estimation of concrete mixes based on TS and GP content, reducing reliance on exhaustive laboratory experimentation and promoting intelligent, resource-efficient design decisions. Moreover, the incorporation of these industrial by-products significantly reduced the environmental impact of concrete production by minimizing carbon emissions and enhancing recyclability compared to traditional concrete blocks. Overall, the findings underscore the viability of transforming waste materials into high-performance, eco-friendly construction components. This work contributes to sustainable construction practices and effective industrial waste management. Future research should explore long-term durability, environmental life cycle assessments, and real-time ML-integrated decision support systems for broader adoption.</p>

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Machine learning-assisted optimization of eco-friendly concrete paving blocks incorporating textile sludge and glass powder for sustainable construction

  • 期刊论文

  • 发表日期：

    2025年8月25日

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  • Anshika Singh,
  • Prince Yadav,
  • Shubham Rai,
  • Vikash Singh

摘要

This study investigates the potential of utilizing textile sludge and glass powder as sustainable materials in the production of environmentally friendly concrete paving blocks. The research aimed to evaluate the feasibility of incorporating varying percentages of textile sludge (0%, 10%, 20%, 30%, and 40% by weight of fine aggregate) and glass powder (0%, 5%, 10%, 15%, and 20% by weight of cement) into the concrete mix, analyzing its effects on the mechanical properties, workability, and durability of the paving blocks. The prepared mix samples were subjected to comprehensive testing, including compressive strength, flexural strength, tensile splitting strength, water absorption, and abrasion resistance, conforming to relevant standards. Results indicated that the addition of textile sludge and glass powder improved the workability of the concrete mix, with optimal mechanical performance achieved at a composite mix of 20% textile sludge and 15% glass powder. This formulation resulted in a compressive strength of 44.34 MPa after 28 days, meeting the standards for high-performance paving blocks. To enhance predictive capability and optimize mix design, a machine learning-based performance model was developed using the XGBoost regression algorithm. The model demonstrated high predictive accuracy (R² >0.95) across multiple target variables, including compressive, flexural, and tensile strength, as well as water absorption. This data-driven approach enabled rapid performance estimation of concrete mixes based on TS and GP content, reducing reliance on exhaustive laboratory experimentation and promoting intelligent, resource-efficient design decisions. Moreover, the incorporation of these industrial by-products significantly reduced the environmental impact of concrete production by minimizing carbon emissions and enhancing recyclability compared to traditional concrete blocks. Overall, the findings underscore the viability of transforming waste materials into high-performance, eco-friendly construction components. This work contributes to sustainable construction practices and effective industrial waste management. Future research should explore long-term durability, environmental life cycle assessments, and real-time ML-integrated decision support systems for broader adoption.