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The Brittleness and Chemical Stability of Optimized Geopolymer Composites.

ABSTRACT: Geopolymers are known as high strength and durable construction materials but have a brittle fracture. In practice, this results in a sudden collapse at ultimate load, without any chance of preventing the breakdown of parts or of withstanding the stress for some time. Glass fiber usage as a total anisotropic shape acting as a compact structure component should hinder the fracture mechanism. The optimized compositions in this study led to a significant reinforcement, especially in the case of flexural strength, but also in terms of the compressive strength and notch toughness. The positive and negative influence of the fibers on the complex composite properties provided chemical stability.

SUBMITTER: Steinerova M

PROVIDER: S-EPMC5506892 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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The Brittleness and Chemical Stability of Optimized Geopolymer Composites.

Steinerova Michaela M Matulova Lenka L Vermach Pavel P Kotas Jindrich J

Materials (Basel, Switzerland) 20170409 4

Geopolymers are known as high strength and durable construction materials but have a brittle fracture. In practice, this results in a sudden collapse at ultimate load, without any chance of preventing the breakdown of parts or of withstanding the stress for some time. Glass fiber usage as a total anisotropic shape acting as a compact structure component should hinder the fracture mechanism. The optimized compositions in this study led to a significant reinforcement, especially in the case of fle ...[more]

PMID: 28772756

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Project description:Geopolymers might be the superlative alternative to conventional cement because it is produced from aluminosilicate-rich waste sources to eliminate the issues associated with its manufacture and use. Geopolymer composites (GPCs) are gaining popularity, and their research is expanding. However, casting, curing, and testing specimens requires significant effort, price, and time. For research to be efficient, it is essential to apply novel approaches to the said objective. In this study, compressive strength (CS) of GPCs was anticipated using machine learning (ML) approaches, i.e., one single method (support vector machine (SVM)) and two ensembled algorithms (gradient boosting (GB) and extreme gradient boosting (XGB)). All models' validity and comparability were tested using the coefficient of determination (R2), statistical tests, and k-fold analysis. In addition, a model-independent post hoc approach known as SHapley Additive exPlanations (SHAP) was employed to investigate the impact of input factors on the CS of GPCs. In predicting the CS of GPCs, it was observed that ensembled ML strategies performed better than the single ML technique. The R2 for the SVM, GB, and XGB models were 0.98, 0.97, and 0.93, respectively. The lowered error values of the models, including mean absolute and root mean square errors, further verified the enhanced precision of the ensembled ML approaches. The SHAP analysis revealed a stronger positive correlation between GGBS and GPC's CS. The effects of NaOH molarity, NaOH, and Na2SiO3 were also observed as more positive. Fly ash and gravel size: 10/20 mm have both beneficial and negative impacts on the GPC's CS. Raising the concentration of these ingredients enhances the CS, whereas increasing the concentration of GPC reduces it. Gravel size: 4/10 mm has less favorable and more negative effects. ML techniques will benefit the construction sector by offering rapid and cost-efficient solutions for assessing material characteristics.

Dataset Information

The Brittleness and Chemical Stability of Optimized Geopolymer Composites.

Publications

The Brittleness and Chemical Stability of Optimized Geopolymer Composites.

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