Benefits of Sealed-Curing on Compressive Strength of Fly Ash-Based Geopolymers.
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ABSTRACT:
SUBMITTER: Lee S
PROVIDER: S-EPMC5456872 | biostudies-other | 2016 Jul
REPOSITORIES: biostudies-other
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Project description:Geopolymer concrete is an inorganic concrete that uses industrial or agro by-product ashes as the main binder instead of ordinary Portland cement; this leads to the geopolymer concrete being an eco-efficient and environmentally friendly construction material. A variety of ashes used as the binder in geopolymer concrete such as fly ash, ground granulated blast furnace slag, rice husk ash, metakaolin ash, and Palm oil fuel ash, fly ash was commonly consumed to prepare geopolymer concrete composites. The most important mechanical property for all types of concrete composites, including geopolymer concrete, is the compressive strength. However, in the structural design and construction field, the compressive strength of the concrete at 28 days is essential. Therefore, achieving an authoritative model for predicting the compressive strength of geopolymer concrete is necessary regarding saving time, energy, and cost-effectiveness. It gives guidance regarding scheduling the construction process and removal of formworks. In this study, Linear (LR), Non-Linear (NLR), and Multi-logistic (MLR) regression models were used to develop the predictive models for estimating the compressive strength of fly ash-based geopolymer concrete (FA-GPC). In this regard, a comprehensive dataset consists of 510 samples were collected in several academic research studies and analyzed to develop the models. In the modeling process, for the first time, twelve effective variable parameters on the compressive strength of the FA-GPC, including SiO2/Al2O3 (Si/Al) of fly ash binder, alkaline liquid to binder ratio (l/b), fly ash (FA) content, fine aggregate (F) content, coarse aggregate (C) content, sodium hydroxide (SH)content, sodium silicate (SS) content, (SS/SH), molarity (M), curing temperature (T), curing duration inside ovens (CD) and specimen ages (A) 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 NLR model performed better for predicting the compressive strength of FA-GPC mixtures compared to the other models. Moreover, the sensitivity analysis demonstrated that the curing temperature, alkaline liquid to binder ratio, and sodium silicate content are the most affecting parameter for estimating the compressive strength of the FA-GPC.
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Project description:Every year, a large amount of solid waste such as fly ash and slag is generated worldwide. If these solid wastes are used in concrete mixes to make concrete, it can effectively save resources and protect the environment. The compressive strength of concrete is an essential indicator for testing its quality, and its prediction is affected by many factors. It is difficult to predict its strength accurately. Therefore, based on the current popular machine learning supervised learning algorithms: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVR), three models established a nonlinear mapping between multi-factor features and target feature concrete compressive strength. Using the three completed training models, we validated the test set with 206 example sets, and the Root Mean Square Error (RMSE), fitting coefficient (R2), and Mean Absolute Error (MAE) were used as evaluation metrics. The validation results showed that the values of RMSE, R2, and MAE for the RF model were 0.1, 0.9, and 0.21, respectively; the values of XGBoost model were 0.05, 0.95, and 0.15, respectively. The values of SVR were 0.15, 0.86, and 0.3, respectively. As a result, Extreme Gradient Boosting (XGBoost) has better generalization ability and prediction accuracy than the other two algorithms.
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Project description:Superplasticizers (cement concrete water reducers) are applied to improve the flowability of calcium-rich, alkali-activated materials, with inconsistent results. However, superplasticizer applications are limited in metakaolin-based geopolymers. The possibility of using polycarboxylate superplasticizers and methyl isobutyl carbinol (MIBC) to ameliorate the flowability of metakaolin-based geopolymers was investigated. The ratio of metakaolin, fumed silica, NaOH or KOH, and water in geopolymers at a Na2O or K2O:Al2O3:SiO2:H2O ratio = 1:1:4:10 or 1:1:4:11 was maintained in the formulations. In this study, ether- or ester-based polycarboxylate superplasticizers did not improve the workability of fresh metakaolin-based Na-geopolymers. A low MIBC dose (0.5 wt.% of metakaolin) improved the flowability by 19% and additionally increased the 7-day compressive strength by 22% from 68 to 83 MPa for plain Na-geopolymers. The entrained fine froths produced by adding MIBC during mixing likely reduced friction between metakaolin particles, and the slurry became more workable. Hence, the geopolymer mixture with an improved flowability became more homogenous, which ensured more extensive metakaolin dissolution and hydrolysis. A low MIBC dose could be effective for Na-geopolymers with dual benefits of improved workability and enhanced compressive strength.
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Project description:Replacing a specified quantity of cement with Class F fly ash contributes to sustainable development and reducing the greenhouse effect. In order to use Class F fly ash in self-compacting concrete (SCC), a prediction model that will give a satisfactory accuracy value for the compressive strength of such concrete is required. This paper considers a number of machine learning models created on a dataset of 327 experimentally tested samples in order to create an optimal predictive model. The set of input variables for all models consists of seven input variables, among which six are constituent components of SCC, and the seventh model variable represents the age of the sample. Models based on regression trees (RTs), Gaussian process regression (GPR), support vector regression (SVR) and artificial neural networks (ANNs) are considered. The accuracy of individual models and ensemble models are analyzed. The research shows that the model with the highest accuracy is an ensemble of ANNs. This accuracy expressed through the mean absolute error (MAE) and correlation coefficient (R) criteria is 4.37 MPa and 0.96, respectively. This paper also compares the accuracy of individual prediction models and determines their accuracy. Compared to theindividual ANN model, the more transparent multi-gene genetic programming (MGPP) model and the individual regression tree (RT) model have comparable or better prediction accuracy. The accuracy of the MGGP and RT models expressed through the MAE and R criteria is 5.70 MPa and 0.93, and 6.64 MPa and 0.89, respectively.
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Project description:This study reports the preparation of geopolymers with a mechanical performance similar to that of cement at room temperature by ground fly ash mixed with a small amount of cement. The grinding time of fly ash raw materials was 0,20,40 and 60 min, respectively. The influence of the grinding degree of the fly ash on the properties and the reaction degree of the geopolymer were investigated by XRD, SEM, EDS, and mercury compression tests. The reaction degree of the fly ash geopolymer was quantified by the selective dissolution method. Increasing the grinding degree of fly ash significantly increased the compressive strength of the geopolymer and the density of the microstructure of materials also increased. Furthermore, porosity and the average pore size decreased and the proportion of small holes in the pores gradually increased. The calculation results were in coincidence with the compressive strength test and the micro-performance test of the material, thus indicating that the selective dissolution method can reflect the influence of the grinding degree on the reaction degree of the geopolymer. Furthermore, the reaction degree of the geopolymer increased as the grinding degree of the fly ash increased. However, the growth rate of the reaction degree for the geopolymer slowed down when the fly ash was ground for more than 40 min.
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Project description:This paper assesses the feasibility of two industrial wastes, fly ash (FA) and rice husk ash (RHA), as raw materials for the production of geopolymeric pastes. Three typologies of samples were thus produced: (i) halloysite activated with potassium hydroxide and nanosilica, used as the reference sample (HL-S); (ii) halloysite activated with rice husk ash dissolved into KOH solution (HL-R); (iii) FA activated with the alkaline solution realized with the rice husk ash (FA-R). Dense and porous samples were produced and characterized in terms of mechanical properties and environmental impact. The flexural and compressive strength of HL-R reached about 9 and 43 MPa, respectively. On the contrary, the compressive strength of FA-R is significantly lower than the HL-R one, in spite of a comparable flexural strength being reached. However, when porous samples are concerned, FA-R shows comparable or even higher strength than HL-R. Thus, the current results show that RHA is a valuable alternative to silica nanopowder to prepare the activator solution, to be used either with calcined clay and fly ash feedstock materials. Finally, a preliminary evaluation of the global warming potential (GWP) was performed for the three investigated formulations. With the mix containing FA and RHA-based silica solution, a reduction of about 90% of GWP was achieved with respect to the values obtained for the reference formulation.
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Project description:Geopolymers are inorganic polymers formed from the alkaline activation of amorphous alumino-silicate materials resulting in a three-dimensional polymeric network. As a class of materials, it is seen to have the potential of replacing ordinary Portland cement (OPC), which for more than a hundred years has been the binder of choice for structural and building applications. Geopolymers have emerged as a sustainable option vis-à-vis OPC for three reasons: (1) their technical properties are comparable if not better; (2) they can be produced from industrial wastes; and (3) within reasonable constraints, their production requires less energy and emits significantly less CO₂. In the Philippines, the use of coal ash, as the alumina- and silica- rich geopolymer precursor, is being considered as one of the options for sustainable management of coal ash generation from coal-fired power plants. However, most geopolymer mixes (and the prevalent blended OPC) use only coal fly ash. The coal bottom ash, having very few applications, remains relegated to dumpsites. Rice hull ash, from biomass-fired plants, is another silica-rich geopolymer precursor material from another significantly produced waste in the country with only minimal utilization. In this study, geopolymer samples were formed from the mixture of coal ash, using both coal fly ash (CFA) and coal bottom ash (CBA), and rice hull ash (RHA). The raw materials used for the geopolymerization process were characterized using X-ray fluorescence spectroscopy (XRF) for elemental and X-ray diffraction (XRD) for mineralogical composition. The raw materials' thermal stability and loss on ignition (LOI) were determined using thermogravimetric analysis (TGA) and reactivity via dissolution tests and inductively-coupled plasma mass spectrometry (ICP) analysis. The mechanical, thermal and microstructural properties of the geopolymers formed were analyzed using compression tests, Fourier transform infra-red spectroscopy (FTIR), scanning electron microscopy (SEM) and thermogravimetric analysis (TGA). Using a Scheffé-based mixture design, targeting applications with low thermal conductivity, light weight and moderate strength and allowing for a maximum of five percent by mass of rice hull ash in consideration of the waste utilization of all three components, it has been determined that an 85-10-5 by weight ratio of CFA-CBA-RHA activated with 80-20 by mass ratio of 12 M NaOH and sodium silicate (55% H₂O, modulus = 3) produced geopolymers with a compressive strength of 18.5 MPa, a volumetric weight of 1660 kg/m³ and a thermal conductivity of 0.457 W/m-°C at 28-day curing when pre-cured at 80 °C for 24 h. For this study, the estimates of embodied energy and CO₂ were all below 1.7 MJ/kg and 0.12 kg CO₂/kg, respectively.
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