Influence of Cooking Oil on the Mitigation of Autogenous Shrinkage of Alkali-Activated Slag Concrete

Huang, Jinguang; Yan, Jie; Liu, Kaihua; Wei, Bin; Zou, Chaoying

doi:10.3390/ma13214907

Cited by 11 publications

(3 citation statements)

References 51 publications

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“…Superabsorbent polymers, lightweight aggregates, and zeolites can all provide internal curing after pre-absorption [95][96][97]. Other admixtures that can alter the reaction kinetics and microstructure of AAMs can also modify their shrinkage behaviors, but the development of strength can be compromised if the reaction is delayed [47,98].…”

Section: Shrinkagementioning

confidence: 99%

Recycling of Aluminosilicate-Based Solid Wastes through Alkali-Activation: Preparation, Characterization, and Challenges

Feng,

Yi,

Zhao

et al. 2024

Buildings

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Recycling aluminosilicate-based solid wastes is imperative to realize the sustainable development of constructions. By using alkali activation technology, aluminosilicate-based solid wastes, such as furnace slag, fly ash, red mud, and most of the bio-ashes, can be turned into alternative binder materials to Portland cement to reduce the carbon footprint of the construction and maintenance activities of concrete structures. In this paper, the chemistry involved in the formation of alkali-activated materials (AAMs) and the influential factors of their properties are briefly reviewed. The commonly used methods, including X-ray diffraction (XRD), scanning electron microscopy (SEM), thermogravimetric analysis (TG), nuclear magnetic resonance spectroscopy (NMR), and X-ray pair distribution function technology, to characterize the microstructure of AAMs are introduced. Typical characterization results of AAMs are shown and the limitations of each method are discussed. The main challenges, such as shrinkage, creep, efflorescence, carbonation, alkali–silica reaction, and chloride ingress, to conquer for a wider application of AAMs are reviewed. It is shown that several performances of AAMs under certain circumstances seem to be less satisfactory than traditional portland cement systems. Existing strategies to improve these performances are reviewed, and recommendations for future studies are given.

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Section: Shrinkagementioning

confidence: 99%

Recycling of Aluminosilicate-Based Solid Wastes through Alkali-Activation: Preparation, Characterization, and Challenges

Feng,

Yi,

Zhao

et al. 2024

Buildings

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“…In this study, a data bank was first built by conducting a literature review to collect extensive and detailed information on the mix proportions, physical properties of composites, and mechanical properties of AASC. In total, 181 sets of AASC design mix produced with different mix proportions were gathered from different reliable sources consisting of previously published or available studies on the behaviour of AASC [30,[47][48][49][50][51][52][53][54][55][56][57][58][59][60][61]. As the data obtained for this study were mainly from various sources, the developed ANN-based prediction model would be more general.…”

Section: Data Bank Constructionmentioning

confidence: 99%

Artificial Neural Network-Forecasted Compression Strength of Alkaline-Activated Slag Concretes

et al. 2022

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The utilization of ordinary Portland cement (OPC) in conventional concretes is synonymous with high carbon emissions. To remedy this, an environmentally friendly concrete, alkaline-activated slag concrete (AASC), where OPC is completely replaced by ground granulated blast-furnace slag (GGBFS) industrial waste, is one of the currently pursued research interests. AASC is not commonly used in the construction industry due to limitations in experience and knowledge on the mix proportions and mechanical properties. To circumvent great labour in the experimental works toward the determination of the optimal properties, this study, therefore, presents the compressive strength prediction of AASC by employing the back-propagation artificial neural network (ANN) modelling technique. To construct this model, a sufficiently equipped experimental databank was built from the literature covering varied mix proportion effects on the compressive strength of AASC. For this, four model variants with different input parameter considerations were examined and the ideal ANN architecture for each model with the best input number–hidden layer neuron number–output number format was identified to improve its prediction accuracy. From such a setting, the most accurate prediction model with the highest determination coefficient, R2, of 0.9817 was determined, with an ANN architecture of 8-18-1 containing inputs such as GGBFS, a fine to total aggregate ratio, sodium silicate, sodium hydroxide, mixing water, silica modulus of activator, percentage of sodium oxide and water–binder ratio. The prediction accuracy of the optimal ANN model was then compared to existing ANN-based models, while the variable selection was compared to existing AASC models with other machine learning algorithms, due to limitations in the ANN-based model. To identify the parametric influence, the individual relative importance of each input variable was determined through a sensitivity analysis using the connection weight approach, whose results indicated that the silica modulus of the activator and sodium silicate greatly affected the AASC compressive strength. The proposed methodology demonstrates that the ANN-based model can predict the AASC compressive strength with a high accuracy and, consequently, aids in promoting the utilization of AASC in the construction industry as green concrete without performing destructive tests. This prediction model can also accelerate the use of AASC without using a cement binder in the concrete matrix, leading to produce a sustainable construction material.

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“…Rath et al [17] investigated the effect of organic rubber latex on the pore structure and capillary suction of geopolymer concrete and found that the addition of organic rubber latex can effectively decrease the early-age shrinkage of geopolymers. Huang et al [18] reported that the use of edible oil can reduce the surface tension of concrete and make its internal structure denser, thereby effectively reducing the autogenous shrinkage of concrete by 68% without obvious strength loss. In addition, different from other frequently-used measurements for mitigating the shrinkage by adding internal curing admixtures [19][20] or shrinkage-reducing admixtures [11,[21][22], modifying the wettability through organic admixtures can also enable the geopolymer with water repellence and corrosion resistance performance [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Early-age deformation of hydrophobized metakaolin-based geopolymers

Ruan

Chen

Liu

et al. 2023

Cement and Concrete Research

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Influence of Cooking Oil on the Mitigation of Autogenous Shrinkage of Alkali-Activated Slag Concrete

Cited by 11 publications

References 51 publications

Recycling of Aluminosilicate-Based Solid Wastes through Alkali-Activation: Preparation, Characterization, and Challenges

Recycling of Aluminosilicate-Based Solid Wastes through Alkali-Activation: Preparation, Characterization, and Challenges

Artificial Neural Network-Forecasted Compression Strength of Alkaline-Activated Slag Concretes

Early-age deformation of hydrophobized metakaolin-based geopolymers

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