Strengthening mechanisms and machine learning prediction of fiber-reinforced geopolymer composites

Abstract

By incorporating steel fibers (SFs) and polyethylene fibers (PEFs) into the geopolymer matrix to prepare fiber-reinforced geopolymer composites (FRGPC), the strength and toughness of the material can be improved, broadening its application potential in special service environments. This study focuses on analyzing the effects of different fiber types and contents on the thin plate flexural performance of FRGPC. The combined incorporation of the two fibers exhibits a significant hybrid synergistic effect (0.5%SFs; 1.5%PEFs), simultaneously optimizing both strength and toughness. The compressive strength reaches 99.4 MPa, the tensile strain is 2.95%, and the flexural deflection is 27.627 mm, while CO₂ emissions are reduced by 31.09%. The modified flexural toughness evaluation method, based on energy and strength theories, comprehensively reflects the mechanical behavior in terms of flexural strength, energy dissipation, and crack propagation. A Gaussian Process Regression (GPR)-based prediction model for FRGPC flexural toughness was developed, incorporating fiber characteristics, load–deflection curve characteristic parameters, and toughness calculation parameters. The results indicate that the model exhibits high prediction accuracy and reliability for the flexural toughness parameters under small sample data conditions (R² achieves 1.000). This study provides valuable insights into the strengthening and toughening mechanisms of FRGPC and demonstrates the application of GPR for performance prediction, offering a reliable tool for the design of durable and resilient structural components.

Graphical abstract

Keywords

high-strength and high-toughness / geopolymer composites / fiber reinforcement coefficient / toughness evaluation / GPR

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