Effect of freeze-thaw on K-based Geopolymer Concrete (GPC) and Portland Cement Concrete (PCC)

Belforti, Fernanda; Azarsa, Peiman; Gupta, Rishi; Dave, Urmil

doi:10.1201/9780203713143-9

Cited by 8 publications

(12 citation statements)

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“…The specifications of geopolymer concrete represented by i 1 , i 2 , …, i 11 are considered as eleven input features in the input layer of the network. Even though the features are quite commonly used in the context of geopolymer concrete, further details about what these features mean is reported in the literature [ 43 , 44 ]. This is not described here in order to maintain brevity.…”

Section: Artificial Neural Network Architecturementioning

confidence: 99%

Bayesian Regularized Artificial Neural Network Model to Predict Strength Characteristics of Fly-Ash and Bottom-Ash Based Geopolymer Concrete

et al. 2021

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Geopolymer concrete (GPC) offers a potential solution for sustainable construction by utilizing waste materials. However, the production and testing procedures for GPC are quite cumbersome and expensive, which can slow down the development of mix design and the implementation of GPC. The basic characteristics of GPC depend on numerous factors such as type of precursor material, type of alkali activators and their concentration, and liquid to solid (precursor material) ratio. To optimize time and cost, Artificial Neural Network (ANN) can be a lucrative technique for exploring and predicting GPC characteristics. In this study, the compressive strength of fly-ash based GPC with bottom ash as a replacement of fine aggregates, as well as fly ash, is predicted using a machine learning-based ANN model. The data inputs are taken from the literature as well as in-house lab scale testing of GPC. The specifications of GPC specimens act as input features of the ANN model to predict compressive strength as the output, while minimizing error. Fourteen ANN models are designed which differ in backpropagation training algorithm, number of hidden layers, and neurons in each layer. The performance analysis and comparison of these models in terms of mean squared error (MSE) and coefficient of correlation (R) resulted in a Bayesian regularized ANN (BRANN) model for effective prediction of compressive strength of fly-ash and bottom-ash based geopolymer concrete.

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Section: Artificial Neural Network Architecturementioning

confidence: 99%

Bayesian Regularized Artificial Neural Network Model to Predict Strength Characteristics of Fly-Ash and Bottom-Ash Based Geopolymer Concrete

et al. 2021

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“…Initial trial experiments were performed to design a proper mix and to achieve a higher degree of workability (Gupta & Rathod, 2015) (Yang & Gupta, 2018) (Belforti, et al, 2017). Coarse aggregate and sand were locally obtained from a quarry in British Columbia, Canada with relative dry density of 2.671 and 2.713 respectively.…”

Section: Alkaline Solutionsmentioning

confidence: 99%

“…The samples were then kept at an ambient temperature for 24 hrs (approximate relative humidity range of 45% to 70% and approximate temperature range of 5ºC to 15ºC). The samples were demolded after 24 hrs and were steam cured at 80 ºC for 24 hrs (Belforti, et al, 2017).…”

Section: Alkaline Solutionsmentioning

confidence: 99%

Novel approach to microscopic characterization of cryo formation in air voids of concrete

Azarsa

Gupta

2019

Micron

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Portland Cement Concrete (PCC) production is one of the major contributor to atmospheric Carbon Dioxide (CO2) emission. Geopolymer Concrete (GPC) as an alternative construction material has the potential to reduce CO2 emissions while creating durable structures. Freezing and thawing is an exemplary concrete deterioration mechanism that can cause widespread damage in concrete structures. Concrete structures exposed to freeze-thaw cycles delaminate due to expansive stresses induced when liquid converts to ice. There are numerous theoretical studies that have been done focused on capturing the effect of freeze-thaw cycles on microstructure of PCC. However, there is limited and no experimental work reported on cryo formation inside the air voids of PCC and GPC respectively. The main issue here is that most of the scanning electron microscopic devices cannot maintain the low temperature required to capture an image from a frozen sample. The amount of internal stress due to cryo formation and temperature range of cryo formation can be determined by investigation of morphology of the cryo products. Hence, in this study attempts have been made to investigate the morphology of the cryo formation inside the microstructure of GPC using a 4-D Low Temperature Scanning Electron Microscopic (4D-LTSEM). GPC specimens were frozen at -180 ºC and were slowly sublimated to capture cryo creation in the paste. According to ASTM C666, nominal freezing temperature for PCC is -18 ºC. So, the microstructure of GPC at -18 ºC was investigated to find the applicability of ASTM C666 for paste tense. The results show that rate of cryo formation is slow from 0 ºC to -18 ºC indicating sufficient resistance of GPC when exposed to cold climates.

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“…In this study two methods of curing (steam and dry) were used to investigate their influence on compressive strength for GPC made using 50% fly-ash and 50% bottom-ash. This presented mix proportion/ratio used in this study was chosen after considering several other proportions in separate studies [23][24][25] previously completed by the authors and their associates. Sajedi et al [26] compared the compressive strength of 24 mixes cured with various curing methods including room temperature, in water without heating, room temperature after heating 60 • C for duration of 20 h and in water after heating 60 • C for duration of 20 h. According to the results, due to relatively high consistency of slag when exposed to unheated water, the slag-based mortars showed higher compressive strength (80 MPa) at 90 days compared to other mixes.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study Involving Effect of Curing Regime on Elastic Modulus of Geopolymer Concrete

Azarsa

Gupta

2020

Buildings

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Geopolymer Concrete (GPC) as a cement-less construction material has attracted worldwide attention due to its lower carbon footprint. There are numerous studies reported on GPC made using different by-products including fly-ash. However, since the use of bottom-ash is comparatively limited, making potassium-based GPC using this waste can be an alternative to Portland Cement Concrete (PCC). In this study, two methods of accelerated curing were used to determine the influence of elevated temperature on the compressive strength of GPC, composed of 50% bottom-ash and 50% fly-ash. GPC specimens were cured using various temperatures including ambient, 30 °C, 45 °C, 60 °C, and 80 °C for 24 h, all followed by 28 days of ambient curing. The highest compressive strength was obtained with steam curing at a temperature of 80 °C for a duration of 24 h. It is of great significance to evaluate elastic modulus of the concrete mixture so that the short-term rigidity of structures subjected to elongation, bending, or compression can be predicted. In this study, a longitudinal Resonant Frequency Test (RFT) as a non-destructive test (NDT) was used to calculate the elastic modulus of both GPC and a comparative PCC mix. Based on the results, PCC had higher resonant frequency (by about 1000 Hz) compared to GPC. A review of empirical models for predicting GPC’s elastic modulus showed that all of the predicted elastic modulus values were lower than experimental values.

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Effect of freeze-thaw on K-based Geopolymer Concrete (GPC) and Portland Cement Concrete (PCC)

Cited by 8 publications

References 0 publications

Bayesian Regularized Artificial Neural Network Model to Predict Strength Characteristics of Fly-Ash and Bottom-Ash Based Geopolymer Concrete

Bayesian Regularized Artificial Neural Network Model to Predict Strength Characteristics of Fly-Ash and Bottom-Ash Based Geopolymer Concrete

Novel approach to microscopic characterization of cryo formation in air voids of concrete

Comparative Study Involving Effect of Curing Regime on Elastic Modulus of Geopolymer Concrete

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