Systematic Multiscale Models to Predict the Compressive Strength of Cement Paste as a Function of Microsilica and Nanosilica Contents, Water/Cement Ratio, and Curing Ages

Abstract

Sustainable construction requires high-strength cement materials that additives with silica content could provide the requirements as well. In this study, the effect of the micro and nano-size of silica on the compressive strength of cement paste using different mathematical approaches is investigated. This study compares the strength of preferentially replaced cement pastes with microsilica (MS) and nanosilica (NS) incorporation by proposing several mathematical models. In this study, 205 data were extracted from the literature and analyzed. The modeling processes considered the most significant variables as input variables that influence the compression strength, such as curing time, which ranged between 3 and 90 days, the water-cement ratio, which varied between 0.4 and 0.85, and NS ranged between 0 and 15%. MS ranged between 0 and 40% based on the weight of cement. In this process, the compressive strength of cement paste modified with NS and MS was modeled using four different models, including the Linear Regression Model (LR), Nonlinear Model (NLR), Multi-Logistic Regression Model (MLR), and artificial neural network (ANN). The efficiency of the suggested models was evaluated using different statistical assessments, such as the Root Mean Squared Error (RMES), the Mean Absolute Error (MAE), Scatter Index (SI), Objective value (OBJ), and coefficient of determination (R2). The findings revealed that the ANN model conducted better performance for predicting compressive strength for cement paste than the other models based on the statistical assessment. In addition, based on the statistical assessment of the sensitivity of parameters, NS had more of an effect on the compressive strength of cement paste, with 6.3% more than MS.

Soft computing models to predict the compressive strength of GGBS/FA- geopolymer concrete

“…The predicted and observed compressive strengths of GGBS/FA-GPC for training, testing, and validating datasets are shown in Fig 20(A)–20(C) . Similar to other models, it was discovered that the alkaline liquid to binder ratio (l/b) and the sodium silicate to sodium hydroxide ratio of the GGBS/FA-GPC mixture has a significant impact on the compressive strength of the GGBS/FA-GPC, which agrees with experimental findings in the literature [ 66 , 104 – 107 ]. The model parameters are provided in Eq 15 , and the model variables will be chosen using the linear tree registration function.…”

“…Based on the linear regression analysis model, it was discovered that the l/b, SS/SH, and M of the GC mixture have a significant impact on the compressive strength of the GGBS/FA-GPC, which is consistent with experimental data published in the literature. [ 20 , 22 , 66 , 92 , 104 – 107 ]. The equation for the LR model with different weight parameters can be written as follows ( Eq 13 ) and Fig 13 .…”

A Study on the Cement Gel Formation Process during the Creation of Nanomodified High-Performance Concrete Based on Nanosilica

“…Modification with microsilica is currently very popular due to its effectiveness in improving the properties of concrete both separately and in combination with other additives [ 15 , 19 , 26 , 27 , 28 , 39 , 40 , 41 , 42 , 43 ]. The most effective dosages of microsilica vary from 5 to 20%, depending on the raw materials used and the types of concrete.…”

“…The most effective dosages of microsilica vary from 5 to 20%, depending on the raw materials used and the types of concrete. At the same time, the increase in strength characteristics varies from insignificant, when only microsilica is added, to quite impressive (30–80%), when other additives are used along with microsilica, in particular, flask and diatomite [ 27 ], fly ash [ 19 ], carbon nanotubes [ 26 ], nanosilica, and nanosilica sol [ 19 , 41 , 43 , 44 ]. Microsilica densifies and strengthens the structure, increasing its uniformity and stabilizing the cement hydration process [ 19 , 27 , 40 , 41 , 42 , 43 , 44 ].…”

“…The nano-modification of concrete with nanosilica [ 12 , 13 , 18 , 19 , 23 , 24 , 25 , 35 , 41 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ] has already shown efficiency in improving the properties of concrete, but still requires a solution for many issues. The main problem associated with the introduction of nanoparticles into cement-based materials is how to evenly distribute them in the matrix [ 23 ].…”