Mechanical and autogenous healing properties of high-strength and ultra-ductility engineered geopolymer composites reinforced by PE-PVA hybrid fibers

Nguyễn, Huy Hoàng; Nguyễn, Phương Hoàng; Lương, Quang-Hiếu; Meng, Weina; Lee, Bang Yeon

doi:10.1016/j.cemconcomp.2023.105155

Cited by 20 publications

(1 citation statement)

References 51 publications

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“…A database of measurements was compiled from published past studies [ 25 , 26 , 27 , 28 , 29 ]. The database includes 17 EGC samples.…”

Section: Fractal Characteristics Of Cracksmentioning

confidence: 99%

Mechanical Properties and Stress–Strain Relationship of PVA-Fiber-Reinforced Engineered Geopolymer Composite

Zhou,

Li,

Liu

et al. 2024

Polymers

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In this study, seven Engineering Geopolymer Composite (EGC) groups with varying proportions were prepared. Rheological, compressive, flexural, and axial tensile tests of the EGC were conducted to study the effects of the water/binder ratio, the cement/sand ratio, and fiber type on its properties. Additionally, a uniaxial tension constitutive model was established. The results indicate that the EGC exhibits early strength characteristics, with the 7-day compressive strength reaching 80% to 92% of the 28-day compressive strength. The EGC demonstrates high compressive strength and tensile ductility, achieving up to 70 MPa and 4%, respectively. The mechanical properties of the EGC improved with an increase in the sand/binder ratio and decreased with an increase in the water/binder ratio. The stress–strain curve of the EGC resembles that of the ECC, displaying a strain-hardening state that can be divided into two stages: before cracking, the matrix primarily bears the stress; after cracking, the slope decreases, and the fiber predominantly bears the stress.

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“…A database of measurements was compiled from published past studies [ 25 , 26 , 27 , 28 , 29 ]. The database includes 17 EGC samples.…”

Section: Fractal Characteristics Of Cracksmentioning

confidence: 99%

Mechanical Properties and Stress–Strain Relationship of PVA-Fiber-Reinforced Engineered Geopolymer Composite

Zhou,

Li,

Liu

et al. 2024

Polymers

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Predicting Mechanical Properties in Geopolymer Mortars, Including Novel Precursor Combinations, Through XGBoost Method

Yilmaz,

Cakmak,

Kurt

et al. 2024

Arab J Sci Eng

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Concrete is the most widely used material in the building industry due to its affordability, durability, and strength. However, considering carbon emissions, it is believed that concrete will be replaced by geopolymers in the future. As numerous parameters significantly affect the strength of geopolymers, the performance of potential algorithms for strength prediction needs to be evaluated for different binders to select an appropriate algorithm. This study employs machine learning approaches to provide the best prediction method for the flexural strength and compressive strength of geopolymers. A new dataset containing 533 compressive strength and 533 flexural strength values of geopolymers with different binders such as waste glass (GW), obsidian (OB), and fly ash was created. The best prediction solution, with R2 = 0.981 for compressive strength and R2 = 0.898 for flexural strength, was obtained from the extreme gradient boosting (XGBoost) algorithm. Additionally, several other machine learning models were employed, including linear regression, k-nearest neighbors, deep neural network, and random forest, with corresponding determination coefficient (R2) values of 0.763, 0.804, 0.93, and 0.96, respectively. These models were trained and evaluated using a dataset encompassing features such as binder types, age, and heat, to forecast the mechanical properties of geopolymers. Among these models, XGBoost demonstrated the highest R2 value, indicating superior performance in predicting both compressive and flexural strengths. The findings of this study provide valuable insights into the selection of appropriate machine learning algorithms for predicting mechanical properties in geopolymers, thus contributing to advancements in sustainable construction materials.

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Micro-properties and mechanical behavior of high-ductility engineered geopolymer composites (EGC) with recycled concrete and paste powder as green precursor

Wu,

Liu,

Wang

et al. 2024

Cement and Concrete Composites

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Mechanical and autogenous healing properties of high-strength and ultra-ductility engineered geopolymer composites reinforced by PE-PVA hybrid fibers

Cited by 20 publications

References 51 publications

Mechanical Properties and Stress–Strain Relationship of PVA-Fiber-Reinforced Engineered Geopolymer Composite

Mechanical Properties and Stress–Strain Relationship of PVA-Fiber-Reinforced Engineered Geopolymer Composite

Predicting Mechanical Properties in Geopolymer Mortars, Including Novel Precursor Combinations, Through XGBoost Method

Micro-properties and mechanical behavior of high-ductility engineered geopolymer composites (EGC) with recycled concrete and paste powder as green precursor

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