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<p>In the modern era of rapid infrastructure development, the depletion of natural resources and the rise in carbon dioxide (CO₂) emissions have emerged as pressing environmental concerns, driving global warming and ocean acidification. Carbon capture and storage (CCS), also referred to as carbon capture and isolation, has been recognized as an effective strategy to mitigate these challenges by capturing CO₂ emissions from major industrial sources such as cement plants and biomass power facilities. Traditionally, CCS involves the storage of CO₂ in underground geological formations; however, the long-term integration of CO₂ into building materials presents an innovative and sustainable pathway for reducing industrial emissions. In this context, sequester-based carbon mortar offers a promising alternative, enabling the dual benefit of material performance enhancement and carbon mitigation. The present study investigates the mechanical behaviour of sequester-based carbon mortar exposed to acidic conditions, with a focus on compressive, split tensile, and flexural strengths. To complement the experimental program, Artificial Neural Networks (ANNs) were employed to mix composition of the mortar as inputs. The ANN models achieved high correlation coefficients and low mean squared error values, demonstrating their effectiveness in mapping nonlinear relationships between mix composition and mechanical performance. This integration highlights ANN as a robust predictive tool for advancing sustainable construction materials.</p>

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Comprehensive assessment of non-destructive techniques for sequester-based carbon mortar under acidic exposure conditions and predicted using an artificial neural network

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Bhavesh Joshi,
  • Pankaj Sharma,
  • Manish Varma

摘要

In the modern era of rapid infrastructure development, the depletion of natural resources and the rise in carbon dioxide (CO₂) emissions have emerged as pressing environmental concerns, driving global warming and ocean acidification. Carbon capture and storage (CCS), also referred to as carbon capture and isolation, has been recognized as an effective strategy to mitigate these challenges by capturing CO₂ emissions from major industrial sources such as cement plants and biomass power facilities. Traditionally, CCS involves the storage of CO₂ in underground geological formations; however, the long-term integration of CO₂ into building materials presents an innovative and sustainable pathway for reducing industrial emissions. In this context, sequester-based carbon mortar offers a promising alternative, enabling the dual benefit of material performance enhancement and carbon mitigation. The present study investigates the mechanical behaviour of sequester-based carbon mortar exposed to acidic conditions, with a focus on compressive, split tensile, and flexural strengths. To complement the experimental program, Artificial Neural Networks (ANNs) were employed to mix composition of the mortar as inputs. The ANN models achieved high correlation coefficients and low mean squared error values, demonstrating their effectiveness in mapping nonlinear relationships between mix composition and mechanical performance. This integration highlights ANN as a robust predictive tool for advancing sustainable construction materials.