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Experimental study on the properties of ultra-high-strength geopolymer concrete with polypropylene fibers and nano-silica.

ABSTRACT: Because of the recent progress in materials properties, specifically high-strength concrete, further research is needed to evaluate its suitability, understanding, and performance in the modern-day world. This research aims to enhance the performance of ultra-high-strength geopolymer concrete (UHS-GPC) by adding nano-silica (NS) and polypropylene fibers (PPFs). Three 1%, 2%, and 3% different amounts of PPFs and three NS 5%, 10%, and 15% were utilized in the samples. Various performance parameters of UHS-GPC were evaluated, such as fresh property, compressive strength, modulus of elasticity split tensile, flexural and bonding strength, drying shrinkage, load-displacement test, fracture performance, and elevated temperature. The test outcomes showed that by raising the percentage of PPFs and NS to the allowable limit, the performance of UHS-GPC can be improved significantly. The most improved performance of UHS-GPC was obtained at 2% polypropylene fibers and 10% nano-silica, as the compressive, splitting tensile, flexural. Bond strength was improved by 17.07%, 47.1%, 36.52, and 37.58%, and the modulus of elasticity increased by 31.4% at 56 days. The study showed that the sample with 2% PPFs and 10% NS had excellent performance in the load-displacement test, drying shrinkage, fracture behavior, and elevated temperature. At 750°C elevated temperature, the samples' strength was reduced drastically, but at 250°C, the modified samples showed good resistance to heat by retaining their compressive strength to some degree. The present work showed the suitability of PPFs and NS to develop ultra-high-strength geopolymer concrete, which can be used as a possible alternate material for Portland cement-based concrete.

SUBMITTER: Althoey F

PROVIDER: S-EPMC10118105 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Experimental study on the properties of ultra-high-strength geopolymer concrete with polypropylene fibers and nano-silica.

Althoey Fadi F Zaid Osama O Alsulamy Saleh S Martínez-García Rebeca R de Prado-Gil Jesús J Arbili Mohamed M MM

PloS one 20230420 4

Because of the recent progress in materials properties, specifically high-strength concrete, further research is needed to evaluate its suitability, understanding, and performance in the modern-day world. This research aims to enhance the performance of ultra-high-strength geopolymer concrete (UHS-GPC) by adding nano-silica (NS) and polypropylene fibers (PPFs). Three 1%, 2%, and 3% different amounts of PPFs and three NS 5%, 10%, and 15% were utilized in the samples. Various performance parameter ...[more]

PMID: 37079561

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Project description:In this study, compressive strength (CS) of fiber-reinforced nano-silica concrete (FRNSC) was anticipated using ensemble machine learning (ML) approaches. Four types of ensemble ML methods were employed, including gradient boosting, random forest, bagging regressor, and AdaBoost regressor, to achieve the study’s aims. The validity of employed models was tested and compared using the statistical tests, coefficient of determination (R2), and k-fold method. Moreover, a Shapley Additive Explanations (SHAP) analysis was used to observe the interaction and effect of input parameters on the CS of FRNSC. Six input features, including fiber volume, coarse aggregate to fine aggregate ratio, water to binder ratio, nano-silica, superplasticizer to binder ratio, and specimen age, were used for modeling. In predicting the CS of FRNSC, it was observed that gradient boosting was the model of lower accuracy and the AdaBoost regressor had the highest precision in forecasting the CS of FRNSC. However, the performance of random forest and the bagging regressor was also comparable to that of the AdaBoost regressor model. The R2 for the gradient boosting, random forest, bagging regressor, and AdaBoost regressor models were 0.82, 0.91, 0.91, and 0.92, respectively. Also, the error values of the models further validated the exactness of the ML methods. The average error values for the gradient boosting, random forest, bagging regressor, and AdaBoost regressor models were 5.92, 4.38, 4.24, and 3.73 MPa, respectively. SHAP study discovered that the coarse aggregate to fine aggregate ratio shows a greater negative correlation with FRNSC’s CS. However, specimen age affects FRNSC CS positively. Nano-silica, fiber volume, and the ratio of superplasticizer to binder have both positive and deleterious effects on the CS of FRNSC. Employing these methods will promote the building sector by presenting fast and economical methods for calculating material properties and the impact of raw ingredients.

Dataset Information

Experimental study on the properties of ultra-high-strength geopolymer concrete with polypropylene fibers and nano-silica.

Publications

Experimental study on the properties of ultra-high-strength geopolymer concrete with polypropylene fibers and nano-silica.

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