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<p>Basalt fibers have gained increasing attention as a sustainable and high-performance reinforcement for concrete. This study aims to quantitatively evaluate the mechanical and fresh properties of basalt fiber-reinforced concrete (BFRC) and to develop an AI-supported predictive framework for optimizing fiber dosage. The experimental program examined fiber contents ranging from 0% to 2.0% by weight of cement and assessed compressive, split tensile, flexural strengths, and workability at curing ages of 7, 28, and 56 days. To overcome the limitations of discrete experimental intervals, a hybrid data-driven method employing Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) and bootstrap resampling was implemented to generate high-resolution strength predictions across 0.1%–2.0% fiber increments and extended curing ages (3, 14, and 56 days). The results revealed significant increases in all mechanical properties up to an optimal fiber dosage of 1.5%, with compressive, tensile, and flexural strengths reaching 50.2&#xa0;MPa, 5.7&#xa0;MPa, and 8.2&#xa0;MPa at 56 days, respectively. Beyond this dosage, a slight reduction was observed due to fiber agglomeration and reduced workability. The AI-assisted interpolation accurately captured the nonlinear strength–dosage relationships and produced confidence-bounded predictions that align closely with experimental trends. The novelty of this study lies in its hybrid experimental–AI approach, which combines laboratory results with shape-preserving interpolation and uncertainty quantification to provide a comprehensive, fine-resolution performance map for BFRC. This framework enhances precision in determining optimal fiber dosages and reduces reliance on exhaustive laboratory trials, offering a practical tool for performance-driven concrete mix design. This work addresses key challenges in BFRC research, including nonlinear dosage–response behaviour, limited experimental resolution, and the absence of reliable AI-supported prediction models capable of quantifying uncertainty.</p> Graphical abstract <p></p>

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Experimental investigation of basalt fiber-reinforced concrete: enhancing strength and sustainability

  • 期刊论文

  • 发表日期：

    2026年4月16日

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  • Aditya Agrawal,
  • Narayan Malviya

摘要

Basalt fibers have gained increasing attention as a sustainable and high-performance reinforcement for concrete. This study aims to quantitatively evaluate the mechanical and fresh properties of basalt fiber-reinforced concrete (BFRC) and to develop an AI-supported predictive framework for optimizing fiber dosage. The experimental program examined fiber contents ranging from 0% to 2.0% by weight of cement and assessed compressive, split tensile, flexural strengths, and workability at curing ages of 7, 28, and 56 days. To overcome the limitations of discrete experimental intervals, a hybrid data-driven method employing Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) and bootstrap resampling was implemented to generate high-resolution strength predictions across 0.1%–2.0% fiber increments and extended curing ages (3, 14, and 56 days). The results revealed significant increases in all mechanical properties up to an optimal fiber dosage of 1.5%, with compressive, tensile, and flexural strengths reaching 50.2 MPa, 5.7 MPa, and 8.2 MPa at 56 days, respectively. Beyond this dosage, a slight reduction was observed due to fiber agglomeration and reduced workability. The AI-assisted interpolation accurately captured the nonlinear strength–dosage relationships and produced confidence-bounded predictions that align closely with experimental trends. The novelty of this study lies in its hybrid experimental–AI approach, which combines laboratory results with shape-preserving interpolation and uncertainty quantification to provide a comprehensive, fine-resolution performance map for BFRC. This framework enhances precision in determining optimal fiber dosages and reduces reliance on exhaustive laboratory trials, offering a practical tool for performance-driven concrete mix design. This work addresses key challenges in BFRC research, including nonlinear dosage–response behaviour, limited experimental resolution, and the absence of reliable AI-supported prediction models capable of quantifying uncertainty.

Graphical abstract