Optimum Mixture Design of Bottom Ash Based Alkali-Activated Mortar Using Artificial Neural Networks

Su Tae Kang; Hyun Jin Kang; Gum Sung Ryu; Gyung Taek Koh; Jang Hwa Lee

doi:10.4028/www.scientific.net/KEM.452-453.733

Paper Titles

Evaluation of Fundamental UHPC Properties According to the Shape of Steel Fiber
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Mechanical Characteristics Analysis of Alkali-Activated Fly Ash Cementitious Materials
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The Influence of Chemical Admixtures on the Autogenous Shrinkage Ultra-High Performance Concrete
p.725

Computer Modelling of Crack Propagation in Porous Reactor Core Graphite
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Optimum Mixture Design of Bottom Ash Based Alkali-Activated Mortar Using Artificial Neural Networks
p.733

Relationship between Microscopic Structures and Compressive Strength of Alkali-Activated Fly Ash Mortar
p.737

Investigation of Surface Crack Types under Axial Compression Loading for a Case-Hardening Steel
p.741

Effect of Humidity on Fatigue Strength of Age-Hardened Al Alloy under Rotating Bending
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Giga-Cycle Fatigue Behavior of Notched Specimens for High Speed Steel
p.749

HomeKey Engineering MaterialsKey Engineering Materials Vols. 452-453Optimum Mixture Design of Bottom Ash Based...

Optimum Mixture Design of Bottom Ash Based Alkali-Activated Mortar Using Artificial Neural Networks

Abstract:

Bottom ash based alkali-activated mortar is prepared by incorporating sodium hydroxide and sodium silicate with some additional water if needed, and is activated with temperature curing. This research was conducted to derive an optimum mixture design of the bottom ash based alkali-activated mortar. The experimental studies were first performed to estimate the effect of the added water content, alkali activator to bottom ash ratio, sodium silicate to sodium hydroxide ratio as well as curing temperature on workability and strength. In order to optimize the mix proportion, based on the experimental results, artificial neural networks were introduced.

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Periodical:

Key Engineering Materials (Volumes 452-453)

Pages:

733-736

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.452-453.733

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Online since:

November 2010

Authors:

Su Tae Kang, Hyun Jin Kang, Gum Sung Ryu, Gyung Taek Koh, Jang Hwa Lee

Keywords:

Alkali-Activated, Artificial Neural Network (ANN), Bottom Ash, Strength, Workability

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References

[1] W.K.W. Lee and J.S.J. Van Deventer, in: Structure reorganization of class F fly ash in alkaline silicate solutions, Collides and Surfaces A: Physicochemistry Engineering Aspects, Vol. 221, 2002, pp.49-66.

DOI: 10.1016/s0927-7757(02)00237-6

Google Scholar

[2] B.R. Hart, M.A. Powell, W.S. Fyfe, in: Geochemistry and Mineralogy of fly-ash from the Mae Moh lignite deposit, Energy sources, Vol. 17, 1995, pp.23-40.

DOI: 10.1080/00908319508946069

Google Scholar

[3] M. Cheriaf, J.C. Rocha, J. Péra, in: Pozzolanic properties of pulverized coal combustion bottom ash, Cement and Concrete Research, Vol. 29, 1999, pp.1387-1391.

DOI: 10.1016/s0008-8846(99)00098-8

Google Scholar