Predicting Compressive Strength of Sustainable Self-Consolidating Concrete Using Random Forest

Osama Ahmed Mohamed; Modafar Ati; Omar Fawwaz Najm

doi:10.4028/www.scientific.net/KEM.744.141

Paper Titles

Effect of Charcoal Ash Coconut Shell from Waste Material at Different Size on the Physical Properties of Bitumen
p.121

Influence of Hydrothermal Nanosilica on Mechanical Properties of Plain Concrete
p.126

Effect of Partial Replacement of Fly Ash by Metakaolin on Strength Development of Fly Ash Based Geopolymer Mortar
p.131

Validation of the Splitting Tensile Strength Formula for Concrete Containing Blast Furnace Slag
p.136

Predicting Compressive Strength of Sustainable Self-Consolidating Concrete Using Random Forest
p.141

Experiments and Optimization of Mix Proportions for Cement Paste Backfill Materials with Extra-Fine Unclassified Tailings
p.146

Numerical Study of Fracture Process on Full-Scale Concrete Foundations by Means of Controlled Blast Method Utilizing Galvanized Steel Charge Holders
p.152

Transient Analysis on Reflective Crack of Highway Semi-Rigid Pavement Caused by Temperature Change
p.163

HomeKey Engineering MaterialsKey Engineering Materials Vol. 744Predicting Compressive Strength of Sustainable...

Predicting Compressive Strength of Sustainable Self-Consolidating Concrete Using Random Forest

Abstract:

This paper demonstrates the application of Random Forest (RF) algorithm for prediction of compressive strength of sustainable self-consolidating concrete (SCC) in which significant amount of cement was replaced with minerals such as fly ash, ground granulated blast furnace slag (GGBS), and silica fume. SCC improves the quality of the finished concrete product and is considered an environmentally friendly alternative to conventional concrete. RF proved capable of predicting compressive strength with high accuracy. The ability of RF algorithm to predict compressive strength established confidence on the experimental data itself which can be used for further studies on properties of self-consolidating concrete. The high level of accuracy in predicting essential engineering properties of concrete through RF algorithms offers important opportunities to enhance quality in ready mix production industry.

You might also be interested in these eBooks

Symposium on Materials Science and Engineering

View Preview

Info:

Periodical:

Key Engineering Materials (Volume 744)

Pages:

141-145

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.744.141

Citation:

Cite this paper

Online since:

July 2017

Authors:

Osama Ahmed Mohamed*, Modafar Ati, Omar Fawwaz Najm

Keywords:

Basalt Fibers, Fly Ash, GGBS, Mineral Admixtures, Prediction, Random Forest, Self-Consolidating Concrete, Silica Fume, Sustainable Concrete

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] O. A. Mohamed, Z. I. Syed, O. F. Najm, Splitting tensile strength of sustainable self-consolidating concrete, Proc. Eng. (Elsevier), 145 (2016) 1218–1225.

DOI: 10.1016/j.proeng.2016.04.157

Google Scholar

[2] O. A. Mohamed, W. Al-Hawat, Influence of fly ash and basalt fibers on strength and chloride penetration resistance of self-consolidating concrete, Mater. Sci. Forum. 866 (2016) 3-8.

DOI: 10.4028/www.scientific.net/msf.866.3

Google Scholar

[3] L. Breiman, Random forests, Mach. Learn. 45(1) (2001) 5-32.

Google Scholar

[4] K. Kenji, L. A. Rendell, The feature selection problem: Traditional methods and a new algorithm, AAAI, 2 (1992) 129-134.

Google Scholar

[5] K. Igor, Estimating attributes: analysis and extensions of RELIEF, European conference on machine learning, 784 (1994) 171-182.

Google Scholar