Fracture Properties of Geopolymer Concrete Based on Metakaolin, Fly Ash and Rice Rusk Ash

Pires, Eliane Fernandes Côrtes; Azevedo, Cláudio Mesquita Campinho de; Pimenta, Alexandre Dias; Silva, Felipe José da; Darwish, Fathi Aref

doi:10.1590/1980-5373-mr-2016-0974

Cited by 16 publications

(6 citation statements)

References 12 publications

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“…Despite all these recent developments, geopolymers still suffer from sudden unstable fractures due to their extreme brittleness [31,32]. The excessive low resistance to crack initiation/propagation is not due only to the fragility of the geopolymer product of reaction, but also to the extent of porosity and crack production during hardening.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Performance of Glass-Based Geopolymer Matrix Composites Reinforced with Cellulose Fibers

2018

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Glass-based geopolymers, incorporating fly ash and borosilicate glass, were processed in conditions of high alkalinity (NaOH 10–13 M). Different formulations (fly ash and borosilicate in mixtures of 70–30 wt% and 30–70 wt%, respectively) and physical conditions (soaking time and relative humidity) were adopted. Flexural strength and fracture toughness were assessed for samples processed in optimized conditions by three-point bending and chevron notch testing, respectively. SEM was used to evaluate the fracture micromechanisms. Results showed that the geopolymerization efficiency is strongly influenced by the SiO2/Al2O3 ratio and the curing conditions, especially the air humidity. The mechanical performances of the geopolymer samples were compared with those of cellulose fiber–geopolymer matrix composites with different fiber contents (1 wt%, 2 wt%, and 3 wt%). The composites exhibited higher strength and fracture resilience, with the maximum effect observed for the fiber content of 2 wt%. A chemical modification of the cellulose fiber surface was also observed.

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Section: Introductionmentioning

confidence: 99%

Mechanical Performance of Glass-Based Geopolymer Matrix Composites Reinforced with Cellulose Fibers

2018

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“…In order to reduce greenhouse emissions in civil engineering activities, many novel construction materials were developed in recent decades [61]. Unlike the fabrication process of OPC using compounds that release carbon dioxide, GPC uses waste by-product materials which are environment friendly [62], and can be defined as green materials.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches

et al. 2019

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Geopolymer concrete (GPC) is applied successfully in the construction of civil engineering structures. This outcome confirmed that GPC can be used as an alternative material to conventional ordinary Portland cement concrete (OPC). Recent investigations were attempted to incorporate recycled aggregates into GPC to reduce the use of natural materials such as stone and sand. However, traditional methodology used to predict compressive strength and to find out an optimum mix for GPC is yet to be formulated, especially in cases of GPC using by-products as aggregates. In this study, we propose novel hybrid artificial intelligence (AI) approaches, namely a particle swarm optimization (PSO)-based adaptive network-based fuzzy inference system (PSOANFIS) and a genetic algorithm (GA)-based adaptive network-based fuzzy inference system (GAANFIS) to predict the 28-day compressive strength of GPC containing 100% waste slag aggregates. To construct and validate these models, 21 different mixes with 210 specimens were casted and tested. Three input parameters were used to predict the tested compressive strength of GPC, i.e., the sodium solution (NaOH) concentration (varied from 10 to 14 M), the mass ratio of alkaline activation solution to fly ash (AAS/FA), ranging from 0.4 to 0.5, and the mass ratio of sodium silicate (Na2SiO3) to sodium hydroxide solution (SS/SH) which was varied from 2 to 3. The compressive strength of the fabricated GPC was used as output parameter for the prediction models. Validation of the models was done using several statistical criteria such as mean absolute error (MAE), root-mean-square error (RMSE), and correlation coefficient (R). The results show that the PSOANFIS and GAANFIS models have strong potential for predicting the 28-day compressive strength of GPC, but the PSOANFIS (MAE = 1.847 MPa, RMSE = 2.251 MPa, and R = 0.934) was slightly better than the GAANFIS (MAE = 2.115 MPa, RMSE = 2.531 MPa, and R = 0.927). This study will help in reducing the time and cost for the implementation of laboratory experiments in designing the optimum proportions of GPC.

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“…In 1991, Davidovits [ 2 ] prepared a new aluminosilicate inorganic polymer, referred to as a geopolymer, by combining a solid aluminosilicate with an alkali metal silicate solution in an alkaline environment. Over the past three decades, materials such as fly ash, slag, metakaolin, waste glass and biomass ash have been used with an alkali activator to synthesize geopolymers in order to replace cement [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. The mechanical properties of geopolymers prepared with pure fly ash were generally poor; however, when the fly ash was partially substituted with slag, the mechanical properties, micro-structure and erosion resistance of the geopolymers were significantly enhanced [ 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Fly Ash-Slag Based Geopolymer Paste Based on Multi-Optimized Artificial Neural Network

et al. 2023

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The fly ash-slag geopolymer is regarded as one of the new green cementitious materials that can replace cement, but it is difficult to predict its mechanical properties by conventional methods. Therefore, in the present study, the back propagation (BP) artificial neural network technique is used to predict the compressive strength of the fly ash-slag geopolymer. In this paper, data from the published literature were collected as the training set and the experimental results from laboratory experiments were used as the test set. Eight input parameters were determined, as follows: the percentage of fly ash, the percentage of slag, the water–cement ratio, the curing age, the modulus of alkali activator, the mass ratio of NaOH to Na2SiO3 and the moles of Na2O and SiO2 in the alkali activator. Three multilayer artificial neural network models were constructed using the Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG) algorithms to compare the prediction accuracy of the compressive strength of the fly ash-slag geopolymer paste at different ages (3, 7, and 28 d). It was concluded that the training set error of the BR–BP neural network was the smallest. Ultimately, the hyperparameter optimization of the BR–BP neural network was carried out to compare the training set and the test set errors before and after the optimization, and the results show that the BR–BP neural network model with hyperparameter optimization had the highest prediction accuracy.

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Fracture Properties of Geopolymer Concrete Based on Metakaolin, Fly Ash and Rice Rusk Ash

Cited by 16 publications

References 12 publications

Mechanical Performance of Glass-Based Geopolymer Matrix Composites Reinforced with Cellulose Fibers

Mechanical Performance of Glass-Based Geopolymer Matrix Composites Reinforced with Cellulose Fibers

Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches

Prediction of Compressive Strength of Fly Ash-Slag Based Geopolymer Paste Based on Multi-Optimized Artificial Neural Network

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