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

Use of pumice stone and silica fume as precursor material for the design of a geopolymer

Background

Geopolymers are alternative materials to cement because they require less energy in their production process; hence, they contribute to the reduction in CO 2 emissions. This study aims to evaluate the possibility of using industrial residues such as silica fume (SF) to improve the physical and mechanical properties of a pumice stone (PS)-based geopolymer.

Methods

Through an experimental methodology, the process starts with the extraction, grinding, and sieving of the raw material to carry out the physical and chemical characterization of the resulting material, followed by the dosage of the geopolymer mixture considering the factors that influence the resistance mechanical strength. Finally, the physical and mechanical properties of the geopolymer were characterized. This research was carried out in four stages: characterization of the pumice stone, design of the geopolymer through laboratory tests, application according to the dosage of the concrete, and analysis of the data through a multi-criteria analysis.

Results

It was determined that the optimal percentage of SF replacement is 10%, which to improves the properties of the geopolymer allowing to reach a maximum resistance to compression and flexion of 14.10 MPa and 4.78 MPa respectively, showing that there is a direct relationship between the percentage of SF and the resistance.

Conclusions

Geopolymer preparation involves the use of PS powder with a composition rich in silicon and aluminum. The factors influencing strength include the ratio of sodium silicate to sodium hydroxide, water content, temperature, curing time, molarity of sodium hydroxide, and binder ratio. The results showed an increase in the compression and flexural strength with 10% SF replacement. The geopolymer's maximum compressive strength indicates its non-structural use, but it can be improved by reducing the PS powder size.

Study on the Reactivity Activation of Coal Gangue for Efficient Utilization

In this study, the research aim is to enhance the activity index of activated coal gangue and study its activation mechanism. The activation process of coal gangue was optimized through orthogonal tests, and the Back-Propagation (BP) neural network model was improved using a genetic algorithm. With the effects of grinding duration, calcination temperature, and calcination duration, the morphological changes and phase transformation processes of coal gangue were studied at the micro and meso levels to clarify the activation mechanism. The results indicated that the effect of calcination temperature on the strength activity index of coal gangue was most significant, followed by grinding duration and calcination duration. The potential activity of coal gangue can be effectively stimulated through mechanical and thermal activation, and the content of potential active minerals in coal gangue powders was also increased. The activation process of coal gangue for the optimal scheme was obtained as grinding at 76 min first and thermal treatment at 54 min at 749 °C. As the thermal activation under 950 °C, some unstable external hydroxyls, and internal hydroxyls in kaolinite from coal gangue were removed, the AlⅥ-O octahedron was destroyed, and kaolinite was transformed into spatially disordered metakaolinite with very high activity.

Electrical conductivity, microstructures, chemical compositions, and systematic multivariable models to evaluate the effect of waste slag smelting (pyrometallurgical) on the compressive strength of concrete

Concrete is a composite material widely used in construction. Waste slag smelting (pyrometallurgical) (steel slag (SS)) is a molten liquid melt of silicates and oxides created as a by-product of steel production. It is a complex solution of silicates and oxides. Steel slag recovery conserves natural resources and frees up landfill space. Steel slag has been used in concrete to replace fine and coarse particles (gravel). Three hundred thirty-eight data points were collected, analyzed, and modeled. It was determined which factors influenced the compressive strength of concrete with steel slag replacement in the modeling phase. Water/cement ratio was 0.3-0.872, steel slag content 0-1196 kg/m³, fine aggregate content 175.5-1285 kg/m³, and coarse aggregate content (natural aggregate) 0-1253.75 kg/m³. In addition, 134 data were collected regarding the electrical conductivity of concrete to analyze and model the effect of SS on electrical conductivity. The correlation between compressive strength and electrical conductivity was also observed. This research used a linear regression (LR) model, a nonlinear regression (NLR) model, an artificial neural network (ANN), a full quadratic model (FQ), and an M5P tree model to anticipate the compressive strength of normal strength concrete with steel slag aggregate substitution. For predicting the electrical conductivity, the ANN model was performed. The compressive strength of the steel slag was raised based on data from the literature. Statistical techniques like the dispersion index and Taylor diagram showed that the ANN model with the lowest RMSE predicted compressive strength better than the other models.

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

One of the most science-intensive and developing areas is nano-modified concrete. Its characteristics of high-strength, high density, and improved structure, which is not only important at the stage of monitoring their performance, but also at the manufacturing stage, characterize high-performance concrete. The aim of this study is to obtain new theoretical knowledge and experimental-applied dependencies arising from the “composition–microstructure–properties” ratio of high-strength concretes with a nano-modifying additive of the most effective type. The methods of laser granulometry and electron microscopy are applied. The existing concepts from the point of view of theory and practice about the processes of cement gel formation during the creation of nano-modified high-strength concretes with nano-modifying additives are developed. The most rational mode of the nano-modification of high-strength concretes is substantiated as follows: microsilica ground to nanosilica within 12 h. A complex nano-modifier containing nanosilica, superplasticizer, hyperplasticizer, and sodium sulfate was developed. The most effective combination of the four considered factors are: the content of nanosilica is 4% by weight of cement; the content of the superplasticizer additive is 1.4% by weight of cement; the content of the hyperplasticizer additive is 3% by weight of cement; and the water–cement ratio—0.33. The maximum difference of the strength characteristics in comparison with other combinations ranged from 45% to 57%.

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

A variety of ashes used as the binder in geopolymer concrete such as fly ash (FA), ground granulated blast furnace slag (GGBS), rice husk ash (RHA), metakaolin (MK), palm oil fuel ash (POFA), and so on, among of them the FA was commonly used to produce geopolymer concrete. However, one of the drawbacks of using FA as a main binder in geopolymer concrete is that it needs heat curing to cure the concrete specimens, which lead to restriction of using geopolymer concrete in site projects; therefore, GGBS was used as a replacement for FA with different percentages to tackle this problem. In this study, Artificial Neural Network (ANN), M5P-Tree (M5P), Linear Regression (LR), and Multi-logistic regression (MLR) models were used to develop the predictive models for predicting the compressive strength of blended ground granulated blast furnace slag and fly ash based-geopolymer concrete (GGBS/FA-GPC). A comprehensive dataset consists of 220 samples collected in several academic research studies and analyzed to develop the models. In the modeling process, for the first time, eleven effective variable parameters on the compressive strength of the GGBS/FA-GPC, including the Activated alkaline solution to binder ratio (l/b), FA content, SiO2/Al2O3 (Si/Al) of FA, GGBS content, SiO2/CaO (Si/Ca) of GGBS, fine (F) and coarse (C) aggregate content, sodium hydroxide (SH) content, sodium silicate (SS) content, (SS/SH) and molarity (M) were considered as the modeling input parameters. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the Coefficient of determination (R2) were used to evaluate the efficiency of the developed models. The results indicated that the ANN model better predicted the compressive strength of GGBS/FA-GPC mixtures compared to the other models. Moreover, the sensitivity analysis demonstrated that the alkaline liquid to binder ratio, fly ash content, molarity, and sodium silicate content are the most affecting parameter for estimating the compressive strength of the GGBS/FA-GPC.