Effects of SBR latex and glass fibers on the fracture behavior of geopolymer concrete: an empirical and computational approach

The quasi-brittle nature of Geopolymer Concrete (GPC) severely limits its broader structural viability. This study investigates the optimization of a Ground Granulated Blast-furnace Slag (GGBS) geopolymer concrete through sequential Styrene-Butadiene Rubber (SBR) latex modification and alkali-resistant (AR) glass fiber reinforcement. Polymer dosages from 0% to 25% were evaluated, validating a 15% SBR concentration as the microstructural baseline. Scanning Electron Microscopy and Energy Dispersive X-Ray Analysis (SEM/EDAX) confirmed that this dosage facilitates a monolithic interpenetrating polymer network and enhances Calcium Aluminosilicate Hydrate (C-A-S-H) gel precipitation. Subsequently, AR glass fibers were incorporated (0% to 2.0%) into the optimized SBR-GPC matrix. Empirical testing isolated 1.5% as the absolute optimal fiber dosage, yielding peak 28-day compressive (31.70 MPa), flexural (5.39 MPa), and bearing (62.26 MPa) capacities. Furthermore, single-point loading fracture mechanics testing revealed that the 1.5% integration maximized both fracture energy and the critical stress intensity factor , while also achieving a 50.00% enhancement in ultimate impact energy absorption and reducing chloride permeability to 1350.20 Coulombs (Low). Dosages exceeding 1.5% induced severe agglomeration, systematically degrading structural integrity. Finally, mathematical regression models were compared against advanced Machine Learning architectures like Artificial Neural Networks (ANN) and Gaussian Process Regression (GPR). While simple empirical equations reliably predicted tensile and flexural capacities, the GPR framework demonstrated exceptional superiority in mapping the non-linear compressive strength behavior, achieving a Leave-One-Out Cross-Validation of 0.950.

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Publication Details

Journal
Frattura ed Integrità Strutturale
Published
2026-10-26
DOI
https://doi.org/10.3221/igf-esis.79.08
Primary Topic
Innovative concrete reinforcement materials
Type
article
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article

Effects of SBR latex and glass fibers on the fracture behavior of geopolymer concrete: an empirical and computational approach

Vishal Rohilla, N. Venkata Ramana, Amarnath B. V, R. Vikas
Frattura ed Integrità Strutturale
Innovative concrete reinforcement materials
article

Effects of SBR latex and glass fibers on the fracture behavior of geopolymer concrete: an empirical and computational approach

Vishal Rohilla, N. Venkata Ramana, Amarnath B. V, R. Vikas
article en

Abstract

The quasi-brittle nature of Geopolymer Concrete (GPC) severely limits its broader structural viability. This study investigates the optimization of a Ground Granulated Blast-furnace Slag (GGBS) geopolymer concrete through sequential Styrene-Butadiene Rubber (SBR) latex modification and alkali-resistant (AR) glass fiber reinforcement. Polymer dosages from 0% to 25% were evaluated, validating a 15% SBR concentration as the microstructural baseline. Scanning Electron Microscopy and Energy Dispersive X-Ray Analysis (SEM/EDAX) confirmed that this dosage facilitates a monolithic interpenetrating polymer network and enhances Calcium Aluminosilicate Hydrate (C-A-S-H) gel precipitation. Subsequently, AR glass fibers were incorporated (0% to 2.0%) into the optimized SBR-GPC matrix. Empirical testing isolated 1.5% as the absolute optimal fiber dosage, yielding peak 28-day compressive (31.70 MPa), flexural (5.39 MPa), and bearing (62.26 MPa) capacities. Furthermore, single-point loading fracture mechanics testing revealed that the 1.5% integration maximized both fracture energy and the critical stress intensity factor , while also achieving a 50.00% enhancement in ultimate impact energy absorption and reducing chloride permeability to 1350.20 Coulombs (Low). Dosages exceeding 1.5% induced severe agglomeration, systematically degrading structural integrity. Finally, mathematical regression models were compared against advanced Machine Learning architectures like Artificial Neural Networks (ANN) and Gaussian Process Regression (GPR). While simple empirical equations reliably predicted tensile and flexural capacities, the GPR framework demonstrated exceptional superiority in mapping the non-linear compressive strength behavior, achieving a Leave-One-Out Cross-Validation of 0.950.

Frattura ed Integrità StrutturaleVol. 21(79)
University B.D.T College of Engineering (IN), Hemwati Nandan Bahuguna Garhwal University (IN)
Openalex Percentile: Top 19%
Innovative concrete reinforcement materials
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