A clean dispersant for nano-silica to enhance the performance of cement mortars

Qian, Xin; Wang, Jialai; Wang, Liang; Fang, Yi; Chen, Peiyuan; Li, Mengxiao

doi:10.1016/j.jclepro.2022.133647

Cited by 14 publications

(1 citation statement)

References 32 publications

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“…One of the main benefits of adding the NPs is the ability to stimulate the hydration reactions in the cement during the mixing process; the NPs can be classified as pozzolanic stimulators. The cement consists of Calcium Silicate Hydrates (C–S–H), which are highly evoked by the NPs that increase the rate of its hydration reactions, preventing drying and shrinkage 7,8 . The summary of the data points that were collected has clustered 89% of the total data points at a range of the used NS of 0%–5%; the mix proportions with higher than 5% were not widely tested and analyzed to understand its effect on the mechanical properties of the concrete.…”

Section: Introductionmentioning

confidence: 99%

Analysis and prediction of the effect of Nanosilica on the compressive strength of concrete with different mix proportions and specimen sizes using various numerical approaches

Ali

Muayad

Mohammed

2022

Structural Concrete

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Introducing nanotechnology in concrete is one of the most significant successes in enhancing the mechanical properties of concrete. It affects the quality of the microstructure of the concrete due to its extremely small nanoparticles leading to a faster reaction due to its large surface area. The nanoparticles fill the concrete's pores, resulting in considerably improved strengths in the early ages of the concrete. This research used six models; linear regression (LR), multilinear regression (MLR), nonlinear regression (NLR), pure quadratic (PQ), interaction (IA), and full quadratic (FQ), to predict the compressive strength of the concrete modified with different Nanosilica contents for various mix proportions. The models were applied to three datasets, with 420 collected from several research studies. The ranges of the concrete mix proportions used in this study were as follows, water/cement ratio (w/c) ranged between 0.1 and 1, cement content (C) ranged between 153.81 and 1200 kg/m3, fine aggregate content (FA) ranged between 492 and 2270 kg/m3, coarse aggregate content (CA) ranged between 617 and 2900 kg/m3, superplasticizer (SP) ranged between 0% and 6.7%, coarse aggregate size (CAS) ranged between 6 and 50.8 mm, fine aggregate size (FAS) ranged between 0.025 and 10 mm, Nanosilica content (NS%) ranged between 0% and 15%, w/c of 0.4–0.6 and the curing time (days) ranged between 3 and 180. The compressive strength of the collected datasets ranged from 3 to 120 MPa. The parameters that have improved the analyzed datasets of the compressive strength were the content of Nanosilica and the SP. The models were assessed for their accuracy using multiple assessment criteria; the most evident ones are; the objective function (OBJ), root mean square error (RMSE), Scatter Index (SI), and the maximum absolute error (MAE). The analytical models and the model comparison operations will be shown in detail in this research, indicating that the most effective and accurate model is the FQ model; where coefficient of determination (R2) of 0.96, RMSE of 3.49 MPa, MAE of 2.96 MPa, and SI of 0.08 were conducted from the analytical studies. From the developed models, the compressive strength of the concrete up to a high compressive strength level with high accurately can be predicted. Also, the compressive strength of the concrete can be accurately predicted for various sizes of aggregate and specimens.

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Section: Introductionmentioning

confidence: 99%