Enhancement of strengths of high-calcium fly ash geopolymer containing borax with rice husk ash

2021 ◽  
pp. 102762
Author(s):  
Peem Nuaklong ◽  
Kit Janprasit ◽  
Pitcha Jongvivatsakul
2021 ◽  
Vol 47 (2) ◽  
pp. 324-331
Author(s):  
Prinya Chindaprasirt ◽  
Kiatsuda Somna

Geopolymer is an aluminosilicate material, synthesized from source materials rich in silica and alumina and alkali solution. This product provides similar strength to Portland cement concrete. Geopolymer exhibits a wide variety of properties and characteristics, including high compressive strength, low shrinkage, acid resistance, fire resistance and low thermal conductivity. In term of acid resistance, acid rain is an important consideration due to global warming. Structures deteriorate as a result of persistence contact with acid rain with of pH less than 5. Thus, this research aims to improve acid resistance of fly ash-NaOH geopolymer mortars by incorporating rice husk ash (RHA). Artificial acid rain solution was prepared by mixing nitric acid and sulfuric acid at the ratio of 70:30 v/v. The geopolymer mortars were immersed in 5% nitric acid, 5% sulfuric acid, and 5% synthetic acid rain solutions for 36 weeks. The evaluations of its resistance to acid solution was investigated with surface corrosion, compressive strength, and microstructure. The results showed that the incorporation of RHA improved the acid rain resistance of geopolymer mortar through pore refinement and increase in strength. The mortar with fly ash to RHA ratio of 90:10 provided the highest compressive strength and good resistance to acid rain.


2020 ◽  
Vol 241 ◽  
pp. 118143 ◽  
Author(s):  
Ampol Wongsa ◽  
Ronnakrit Kunthawatwong ◽  
Sakchai Naenudon ◽  
Vanchai Sata ◽  
Prinya Chindaprasirt

2021 ◽  
Vol 295 ◽  
pp. 113140
Author(s):  
Sarah Fernando ◽  
Chamila Gunasekara ◽  
David W. Law ◽  
M.C.M. Nasvi ◽  
Sujeeva Setunge ◽  
...  

2007 ◽  
Vol 21 (6) ◽  
pp. 1356-1361 ◽  
Author(s):  
P. Chindaprasirt ◽  
P. Kanchanda ◽  
A. Sathonsaowaphak ◽  
H.T. Cao

2015 ◽  
Vol 804 ◽  
pp. 129-132
Author(s):  
Sumrerng Rukzon ◽  
Prinya Chindaprasirt

This research studies the potential for using waste ash from industrial and agricultural by-products as a pozzolanic material. Classified fly ash (FA) and ground rice husk ash (RA) were the materials used. Water requirement, compressive strength and porosity of cement mortar were investigated. Test results indicated that FA and RA (waste ash) have a high potential to be used as a good pozzolanic material. The water requirement of mortar mix decreases with the increases in fly ash content. For ground rice husk ash (RA), the water requirement of mortar mix increases with the increases in rice husk ash content. In addition, the reduction in porosity was associated with the increase in compressive strength.


Polymers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 900
Author(s):  
Chamila Gunasekara ◽  
Peter Atzarakis ◽  
Weena Lokuge ◽  
David W. Law ◽  
Sujeeva Setunge

Despite extensive in-depth research into high calcium fly ash geopolymer concretes and a number of proposed methods to calculate the mix proportions, no universally applicable method to determine the mix proportions has been developed. This paper uses an artificial neural network (ANN) machine learning toolbox in a MATLAB programming environment together with a Bayesian regularization algorithm, the Levenberg-Marquardt algorithm and a scaled conjugate gradient algorithm to attain a specified target compressive strength at 28 days. The relationship between the four key parameters, namely water/solid ratio, alkaline activator/binder ratio, Na2SiO3/NaOH ratio and NaOH molarity, and the compressive strength of geopolymer concrete is determined. The geopolymer concrete mix proportions based on the ANN algorithm model and contour plots developed were experimentally validated. Thus, the proposed method can be used to determine mix designs for high calcium fly ash geopolymer concrete in the range 25–45 MPa at 28 days. In addition, the design equations developed using the statistical regression model provide an insight to predict tensile strength and elastic modulus for a given compressive strength.


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