Showing papers on "Sodium silicate published in 2022"

Rheology and microstructure of alkali-activated slag cements produced with silica fume activator

Abstract: The effects of silica fume and sodium silicate-based activators (SFA and SSA, respectively) with different Ms (SiO2/Na2O) values on the setting behavior, rheological, mechanical, and microstructural properties of alkali-activated slag cement (AASC) were investigated. Setting time test results showed that the setting time of AASCs activated by SFA prolonged significantly with an increase of Ms value opposite to SSA activation case. From the rheological point of view, SFA-activated mixtures exhibited a slower structural build-up in the early stage and better workability retention than SSA-activated mixtures. In addition, SFA mixtures showed lower drying shrinkage and slightly higher mechanical properties as compared to SSA mixtures. Microstructure analysis revealed that the mixture produced by SFA with Ms value of 1.2 had less micro-cracks and a well-packed microstructure as compared to the mixtures produced by SSA. The overall evaluation of the test results revealed that SFA could be more economical and sustainable alternative to SSA with its lower cost, much lower CO2 emissions, and more favorable engineering properties.

Rheology and microstructure of alkali-activated slag cements produced with silica fume activator

Abstract: The effects of silica fume and sodium silicate-based activators (SFA and SSA, respectively) with different Ms (SiO2/Na2O) values on the setting behavior, rheological, mechanical, and microstructural properties of alkali-activated slag cement (AASC) were investigated. Setting time test results showed that the setting time of AASCs activated by SFA prolonged significantly with an increase of Ms value opposite to SSA activation case. From the rheological point of view, SFA-activated mixtures exhibited a slower structural build-up in the early stage and better workability retention than SSA-activated mixtures. In addition, SFA mixtures showed lower drying shrinkage and slightly higher mechanical properties as compared to SSA mixtures. Microstructure analysis revealed that the mixture produced by SFA with Ms value of 1.2 had less micro-cracks and a well-packed microstructure as compared to the mixtures produced by SSA. The overall evaluation of the test results revealed that SFA could be more economical and sustainable alternative to SSA with its lower cost, much lower CO2 emissions, and more favorable engineering properties.

Artificial Neural Network-Forecasted Compression Strength of Alkaline-Activated Slag Concretes

Abstract: The utilization of ordinary Portland cement (OPC) in conventional concretes is synonymous with high carbon emissions. To remedy this, an environmentally friendly concrete, alkaline-activated slag concrete (AASC), where OPC is completely replaced by ground granulated blast-furnace slag (GGBFS) industrial waste, is one of the currently pursued research interests. AASC is not commonly used in the construction industry due to limitations in experience and knowledge on the mix proportions and mechanical properties. To circumvent great labour in the experimental works toward the determination of the optimal properties, this study, therefore, presents the compressive strength prediction of AASC by employing the back-propagation artificial neural network (ANN) modelling technique. To construct this model, a sufficiently equipped experimental databank was built from the literature covering varied mix proportion effects on the compressive strength of AASC. For this, four model variants with different input parameter considerations were examined and the ideal ANN architecture for each model with the best input number–hidden layer neuron number–output number format was identified to improve its prediction accuracy. From such a setting, the most accurate prediction model with the highest determination coefficient, R2, of 0.9817 was determined, with an ANN architecture of 8-18-1 containing inputs such as GGBFS, a fine to total aggregate ratio, sodium silicate, sodium hydroxide, mixing water, silica modulus of activator, percentage of sodium oxide and water–binder ratio. The prediction accuracy of the optimal ANN model was then compared to existing ANN-based models, while the variable selection was compared to existing AASC models with other machine learning algorithms, due to limitations in the ANN-based model. To identify the parametric influence, the individual relative importance of each input variable was determined through a sensitivity analysis using the connection weight approach, whose results indicated that the silica modulus of the activator and sodium silicate greatly affected the AASC compressive strength. The proposed methodology demonstrates that the ANN-based model can predict the AASC compressive strength with a high accuracy and, consequently, aids in promoting the utilization of AASC in the construction industry as green concrete without performing destructive tests. This prediction model can also accelerate the use of AASC without using a cement binder in the concrete matrix, leading to produce a sustainable construction material.

Compressive strength of geopolymer concrete composites: a systematic comprehensive review, analysis and modeling

Abstract: The desire to make the concrete industry more environmentally friendly has existed for a long time. Geopolymer concrete, which uses industrial or agricultural by-product ashes as the primary source of binder materials instead of Portland cement, has emerged as a viable building material due to the environmental concerns associated with cement production. One of the most important mechanical parameters for all types of concrete composites, including geopolymer concrete, is compressive strength. This parameter is influenced by a variety of factors, including the alkaline solution to binder ratio, the type and amount of binder, the chemical composition of the binder materials, the amount of aggregate present, the type and amount of alkaline solutions, the ratio of alkaline liquid to binder materials, the curing regime, and the age of the specimens. In this context, a detailed systematic assessment was conducted to demonstrate the effect of these various parameters on the compressive strength of fly ash-based geopolymer concrete (FA-GPC). In addition, multi-scale models such as artificial neural networks, M5P-tree, linear regression, and multi-logistic regression models were developed to predict the compressive strength of FA-GPC composites. Results show that the curing temperature (between 60 °C to 90 °C), sodium silicate to sodium hydroxide ratio (between 1.5 to 2.5), and the alkaline solution to the binder ratio (between 0.35 to 0.5) are those parameters that govern the compressive strength of the FA-GPC. Furthermore, based on the statistical assessment tools, the ANN model has better performance for predicting the compressive strength of FA-GPC than the other developed models as it has the highest value of the coefficient of determination (0.96), lower values of the root mean squared error (3.33), mean absolute error (2.58), objective function value (2.91), and scatter index (0.109).

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

Abstract: 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.

Rheology and structural build-up of sodium silicate- and sodium hydroxide-activated GGBFS mixtures

Abstract: Alkali-activated slag cements (AAS) are considered as an alternative to portland cement (PC) in many studies. A significant number of studies have focused on their mechanical and durability properties, but very few studies have concentrated on their rheological behavior. In this study, the effects of sodium silicate and sodium hydroxide on the rheological properties and structural build-up of alkali-activated GGBFS mixtures have been investigated. The sodium silicate-activated GGBFS mixtures exhibited a lower yield stress and a higher plastic viscosity than sodium hydroxide-activated GGBFS mixtures. The small-amplitude oscillatory shear (SAOS) tests indicated a negligible colloidal interaction between GGBFS particles in sodium silicate-activated system; therefore, the early increase in structuration was associated with the formation of reaction products due to the interaction between the dissolved calcium ions and the silicates originated from the activator solution. On the other hand, the stiffness and the early increase in structural build-up of sodium hydroxide-activated GGBFS system were attributed to the formation of well-percolated network.

Influence of micro Fe2O3 and MgO on the physical and mechanical properties of the zeolite and kaolin based geopolymer mortar

Abstract: Geopolymer binders can be used as a sustainable alternative to Portland cement. Despite the extensive research regarding the influence of different parameters on the properties of geopolymers, the studies investigating the effect of Fe2O3 and MgO on the performance of geopolymer concrete are minimal. This study investigates the impact of micro Fe2O3 and MgO additives on the physical and mechanical properties of the geopolymer binder. For this reason, the binder was developed by replacing 10%, 20%, and 30% kaolin with zeolite. In addition, Fe2O3 and MgO amounts in the binder were increased by substituting 4%, 6%, and 8% Fe2O3 and MgO with zeolite. The binder was activated with NaOH containing 15% Na (Na/binder) by weight. Geopolymer mortar specimens were prepared by mixing binder, sand, and NaOH-water solution, and the physical and mechanical properties of geopolymer specimens were investigated. Results show that replacing zeolite with kaolin and adding micro Fe2O3 and MgO increase the geopolymer specimens' unit weight, compressive strength, flexural strength, and ultrasonic pulse velocity (UPV). Fourier transform infrared spectroscopy (FTIR) and x-ray diffraction (XRD) spectra show no significant difference between the zeolite and zeolite + kaolin based geopolymer specimens. However, hematite mineral phases and periclase and forsterite mineral phases are prominent in geopolymer specimens incorporating micro Fe2O3 and MgO, respectively. Scanning electron microscopy (SEM) analysis shows a dense structure in the zeolite + kaolin based geopolymer specimens than zeolite based geopolymer specimens. Energy dispersive spectroscopy (EDS) analysis shows sodium aluminosilicate and calcium silicate hydrates as the main hydration products of all the geopolymer specimens. Based on the results, adding Kaolin, Fe2O3, and MgO in a zeolite-based geopolymer improves the physical and mechanical properties of the specimens.

Development and Optimization of Geopolymers Made with Desert Dune Sand and Blast Furnace Slag

Abstract: This study assesses the effect of mix design parameters on the fresh and hardened properties, cost, and carbon footprint of geopolymer mortar made with desert dune fines (DDF) and blast furnace slag (BFS). Taguchi method was employed in designing the experiments. Four factors were considered, each having three levels, leading to a total of nine geopolymer mortar mixes. The factors comprised the DDF replacement percentage, alkali-activator solution to binder ratio (AAS/B), sodium silicate-to-sodium hydroxide ratio (SS/SH), and sodium hydroxide (SH) molarity. Ten performance criteria were evaluated, including the flowability, final setting time, hardened density, 1, 7, and 28-day compressive strengths, water absorption, sorptivity, cost, and carbon footprint. ANOVA was carried out to estimate the contribution of each factor towards the response criteria. Further, TOPSIS analysis was utilized to optimize the mixture proportions of DDF-BFS blended geopolymer mortar. Experimental results showed that up to 25% DDF replacement enhanced the density, strength, and durability of the geopolymers with minor impact on the flowability and setting time. Higher replacement percentages had a detrimental impact on the performance but could still be utilized in specific mortar construction applications. The other factors had more limited contributions to the performance, evidenced by the ANOVA. TOPSIS method revealed the optimum mix to be made with DDF replacement of 25%, AAS/B of 0.5, SS/SH of 1.5, and SH molarity of 10 M. Different multivariable regression models were also developed to predict the fresh and hardened properties of the DDF-BFS geopolymer mortars using the mix design parameters.

Improvement of the corrosion performance of AA2024 alloy by a duplex PEO/clay modified sol-gel nanocomposite coating

Abstract: Due to the intrinsic porosity of the layers formed by the plasma electrolytic oxidation (PEO) process, the application of silane-based coatings as an eco-friendly layer is a promising way to diminish penetration of the corrosive species into the PEO coating by pores sealing. In this study, to enhance the corrosion protection of AA2024, the performance of the duplex system achieved by adding different concentrations in sodium montmorillonite (Na-MMT) dispersed into the silane coating was assessed. In this study, a hybrid sol-gel layer (30% V/V) obtained from tetraethoxysilane (TEOS) and 3-glycidoxypropyltrimethoxysilane (GPTMS) sol-gel solution was applied on AA2024 previously covered by an optimized PEO layer using a solution containing sodium silicate and potassium hydroxide solution as electrolyte. Electrochemical impedance spectroscopy (EIS) revealed the significant impact of the sol-gel/clay nanocomposite layer on the corrosion protection performance of the PEO layer on the AA2024 substrate in a 0.1 M NaCl solution. Regarding, the low-frequency impedance of different coating systems upon five weeks exposure to the aggressive solution reported the sealing ability of the sol-gel coatings in which the silane coating modified with 1000 ppm of clay nanoparticles had the maximum corrosion resistance among them (i.e., higher than 107 after 5 weeks immersion). The flake-like structure of sodium montmorillonite not only enhanced the barrier performance, but also FT-IR outcomes reflected the reticulation of the silane network through the interaction of nanoparticles with SiOH groups of the silane layer.