Effects of combined expansive agents and supplementary cementitious materials on the mechanical properties, shrinkage and chloride penetration of self-compacting concrete

Liu, Kaizhi; Shui, Zhonghe; Sun, Tao; Li, Gang; Li, Xiaosheng; Cheng, Shi

doi:10.1016/j.conbuildmat.2019.03.143

Cited by 44 publications

(7 citation statements)

References 34 publications

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“…Best strengths were achieved with 0.04% PVA and 0.02% PAA. This approach enhances self-curing and strength in high-strength concrete [30]. Exploring internal curing in concrete using wood powder and polyethylene glycol 400 (PEG-400).…”

Section: Introductionmentioning

confidence: 99%

Optimizing flow, strength, and durability in high-strength self-compacting and self-curing concrete utilizing lightweight aggregates

Kadhar,

Gopal,

Sivakumar

et al. 2024

Matéria (Rio J.)

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This comprehensive study undertaken to investigate the properties and performance of M60 grade selfcompacting and self-curing concrete mix designs. The research involved an in-depth analysis of various concrete compositions labeled as M1 to M16, each with specific aggregate combinations and percentages. The primary focus was on assessing critical properties such as flow-ability, mechanical, durability and micro structural properties. The mix labeled as M6, featuring a balanced incorporation of fine aggregate alternatives (FAA) and natural coarse aggregates (NCA), exhibited noteworthy behavior in terms of the evaluated properties. This indicated the potential advantages of judiciously combining different types of aggregates to achieve desired concrete characteristics. The research underscored the critical role of aggregate selection and substitution in determining the overall durability, strength, and structural performance of M60 grade concrete. These findings contribute to an improved understanding of optimizing concrete mix designs for achieving enhanced mechanical properties, micro structural and long-term structural sustainability. The mix M12 is more superior compared to all the mix in the form of fresh concrete properties, mechanical properties and durability properties of concrete. This study's outcomes have implications for the construction industry, offering valuable insights into formulating concrete blends tailored for specific structural requirements and desired performance outcomes.

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Section: Introductionmentioning

confidence: 99%

Optimizing flow, strength, and durability in high-strength self-compacting and self-curing concrete utilizing lightweight aggregates

Kadhar,

Gopal,

Sivakumar

et al. 2024

Matéria (Rio J.)

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“…Utilizing supplementary cementitious materials (SCMs) as Portland cement replacement in concrete can have positive environmental effects and relatively lower concrete production costs [1,2]. Examples of SCMs include fly ash (FA), rice husk ash (RHA), silica fume (SF), and granulated blast furnace slag (GBFS) [3][4][5][6][7]. By calcining kaolinite, a highly reactive pozzolanic substance known as metakaolin (MK) is produced.…”

Section: Introductionmentioning

confidence: 99%

Effect of Water Magnetization Technique on the Properties of Metakaolin-Based Sustainable Concrete

Elkerany,

Elshikh,

Elshami

et al. 2023

Construction Materials

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Using metakaolin (MK) in concrete with magnetized water (MW) has a high possibility to enhance concrete suitability. In this study, the effect of using MK and MW on concrete characteristics was studied through testing twelve concrete mixes. Seven ratios of MK were used in this study, namely 0%, 5%, 10%, and 20%, as an alternative to cement and +5%, +10%, and +20% as a cement additive. In addition, five water magnetization methods were applied on MK concrete. In the first stage of this study, the impact of different MK ratios on the workability of concrete, compressive strength, flexural strength, and tensile strength was studied using traditional tap water (TW) as the concrete mixing water. In the second stage, the best mix (best MK ratio) from the first stage was chosen to study the effect of the water magnetization method on concrete properties and to determine the best method for water magnetization. Scanning electronic microscope (SEM) analysis was also carried out on selected mixes to closely investigate the effect of MK and MW on concrete microstructure. The results showed that the best ratio of MK in concrete was +10% (MK as a 10% cement addition), and the best water magnetization method was to pass the water through 1.6 tesla then through 1.4 tesla magnetic fields. The SEM analysis confirmed the absence of pores after using MW instead of regular TW by increasing the calcium silicate hydrate (CSH) gel and reducing calcium hydroxide (CH). Using MK and MW enhanced the compressive strength by up to 33%, 32%, and 27% at 7, 28, and 365 days, respectively, and MW enhanced the workability by up to 3% compared to that of the control mix.

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“…Similarly, the addition of fly ash as partial replacement in cement matrix with a ratio ranging from 15 to 60% can significantly reduce shrinkage due to the slower rate of hydration [ 34 , 35 ]. Additionally, it was shown that calcined clay can decrease short-term autogenous shrinkage but its addition to the composite matrix have a significant effect and thus increases the autogenous shrinkage over a long duration [ 36 , 37 , 38 ], whereas filler materials are often stated to reduce shrinkage by serving as small aggregate and thus decrease the shrinking of the cement paste [ 39 , 40 , 41 ]. It is because of these negative effects that it is vital to investigate the autogenous shrinkage behavior of composite concrete formations, particularly in limestone, slag filler mixtures, HPC, and in making environmentally friendly UHPC [ 37 , 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Autogenous Shrinkage of Concrete Incorporating Super Absorbent Polymer and Waste Materials through Individual and Ensemble Machine Learning Approaches

Qureshi

Saleem²,

Javed

et al. 2022

Materials

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The use of superabsorbent polymers, sometimes known as SAP, is a tremendously efficacious method for reducing the amount of autogenous shrinkage (AS) that occurs in high-performance concrete. This study utilizes support vector regression (SVR) as a standalone machine-learning algorithm (MLA) which is then ensemble with boosting and bagging approaches to reduce the bias and overfitting issues. In addition, these ensemble methods are optimized with twenty sub-models with varying the nth estimators to achieve a robust R2. Moreover, modified bagging as random forest regression (RFR) is also employed to predict the AS of concrete containing supplementary cementitious materials (SCMs) and SAP. The data for modeling of AS includes water to cement ratio (W/C), water to binder ratio (W/B), cement, silica fume, fly ash, slag, the filer, metakaolin, super absorbent polymer, superplasticizer, super absorbent polymer size, curing time, and super absorbent polymer water intake. Statistical and k-fold validation is used to verify the validation of the data using MAE and RMSE. Furthermore, SHAPLEY analysis is performed on the variables to show the influential parameters. The SVM with AdaBoost and modified bagging (RF) illustrates strong models by delivering R2 of approximately 0.95 and 0.98, respectively, as compared to individual SVR models. An enhancement of 67% and 63% in the RF model, while in the case of SVR with AdaBoost, it was 47% and 36%, in RMSE and MAE of both models, respectively, when compared with the standalone SVR model. Thus, the impact of a strong learner can upsurge the efficiency of the model.

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Effects of combined expansive agents and supplementary cementitious materials on the mechanical properties, shrinkage and chloride penetration of self-compacting concrete

Cited by 44 publications

References 34 publications

Optimizing flow, strength, and durability in high-strength self-compacting and self-curing concrete utilizing lightweight aggregates

Optimizing flow, strength, and durability in high-strength self-compacting and self-curing concrete utilizing lightweight aggregates

Effect of Water Magnetization Technique on the Properties of Metakaolin-Based Sustainable Concrete

Prediction of Autogenous Shrinkage of Concrete Incorporating Super Absorbent Polymer and Waste Materials through Individual and Ensemble Machine Learning Approaches

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