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

Abstract

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.

Recent developments in utilization of nano silica in self‐compacting concrete: A review

“…One is that the high pozzolanic activity of NS can promote hydration, the reaction between SiO 2 and cement hydration product Ca(OH) 2 produces C-S-H gels, subsequently the generated C-S-H gels can fill the pores in SCC. 6,29,31,64,84,86,87 Furthermore, nano-particles have great surface energy, and nano-particles are closely combined with cement hydration products, which can promote C-S-H gels to adhere to the NS particles, making the structure of C-S-H gels more stable. 88,89 For instance, after 28-days of curing, compared with the control group (see Figure 10a), the C-S-H gels of SCC containing 3% NS are more evenly distributed (see Figure 10b), and no obvious pores are observed.…”

“…As a SCM for SCC, NS usually plays two roles. One is that the high pozzolanic activity of NS can promote hydration, the reaction between and cement hydration product Ca(OH) 2 produces C‐S‐H gels, subsequently the generated C‐S‐H gels can fill the pores in SCC 6,29,31,64,84,86,87 . Furthermore, nano‐particles have great surface energy, and nano‐particles are closely combined with cement hydration products, which can promote C‐S‐H gels to adhere to the NS particles, making the structure of C‐S‐H gels more stable 88,89 .…”

Artificial rabbit optimization‐based ANFIS model development for predicting the compressive strength of GGBFS‐based concrete

“…All input parameters were determined to have a wide range. 31 Because there was no appreciable overlap in the seven-dimensional input space, statistical analysis showed that the choice of these input variables was reasonable. [32][33][34][35][36] In addition, the histograms in Figure 1 graphically show the distributions of the variables.…”

Effect of modified nano‐titanium and fly ash on ultra‐high‐performance concrete properties

“…Nanotechnology is widely used in construction projects by improving the properties of building materials by modifying or enhancing microstructures at the nanoscale. Furthermore, a small amount of pozzolanic NMs can be used to improve the performance of UHPC by enhancing the mechanical and durability property due to pozzolanic reaction, nucleation effect, and filler 51–53 . As such, a growing number of studies have been conducted on the use of nanoparticles, such as nano‐CaCO 3 , nano‐SiO 2 , nano‐TiO 2 , and nano‐Al 2 O 3 , to enhance the properties of concrete.…”

“…Furthermore, a small amount of pozzolanic NMs can be used to improve the performance of UHPC by enhancing the mechanical and durability property due to pozzolanic reaction, nucleation effect, and filler. [51][52][53] As such, a growing number of studies have been conducted on the use of nanoparticles, such as nano-CaCO 3 , nano-SiO 2 , nano-TiO 2 , and nano-Al 2 O 3 , to enhance the properties of concrete. The use of nano-TiO 2 in concrete works as a photocatalyst and acts as a decomposer for organic pollutants and oxides such as and some oxides (such as NO 2 , NO, and SO 2 ).…”