Predicting the Poisson Ratio of Lightweight Concretes using Artificial Neural Network

• Authors: M. Davraz , Ş. Kilinçarslan , H. Ceylan
• Languages of publication: EN

Abstracts

EN

Artificial neural network is generally information processing system and a computer program that imitates human brain neural network system. By entering the information from outside, artificial neural network can be trained on examples related to a problem, so that modeling of the problem is provided. In this study, compressive strength, Poisson ratio of the lightweight concrete specimens, which have different natural lightweight aggregates, were modeled with artificial neural network. The data which were provided by artificial neural network model were compared with the data obtained from experimental study and a good agreement was determined between the results.

Keywords

EN

62.20.dj

Discipline

• 62.20.dj: Poisson's ratio

• Publisher: Institute of Physics, Polish Academy of Sciences
• Journal: Acta Physica Polonica A
• Year: 2015
• Volume: 128
• Issue: 2B
• Pages: B-184-B-186

Dates

published

2015-8

Contributors

author

M. Davraz

• Suleyman Demirel University, Natural and Industrial Building Material Application and Research Center, Isparta, Turkey

author

Ş. Kilinçarslan

• Suleyman Demirel University, Natural and Industrial Building Material Application and Research Center, Isparta, Turkey

author

H. Ceylan

• Suleyman Demirel University, Department of Construction, Technical Sciences Vocation School, Isparta, Turkey

References

• [1] S.C. Kok, Z. Min-Hong, Cement Concrete Res. 25, 276 (2002)
• [2] J. Hertz, A. Krogh, R. Palmer, Introduction to the Theory of Neural Networks, Addison-Wesley, Redwood City 1991
• [3] A. Oztaş, Constr. Build Mater. 20, 769 (2006)
• [4] TS EN 12390-7, Testing hardened concrete-Part 7: Density of hardened concrete, TSE, Ankara 2002
• [5] TS EN 12390-3, Testing hardened concrete-Part 3: Compressive strength of test specimens, TSE, Ankara 2003
• [6] TS EN 12390-5, Testing hardened concrete-Part 5: Flexural strength of test specimens, TSE, Ankara 2002
• [7] TS 3502, Test method for static modulus of elasticity and Poisson ratio of concrete in compression, TSE, Ankara 1981
• [8] M. Davraz, H. Ceylan, S. Kılıncarslan, Acta Phys. Pol. A 127, 1246 (2015), doi: 10.12693/APhysPolA.127.1246
• [9] C. Basyigit, I. Akkurt, S. Kılıncarslan, A. Beycioglu, Neural Comput. Appl. 19, 507 (2010)
• [10] S. Terzi, Construct. Build Mater. 21, 590 (2007)

Identifiers

DOI

10.12693/APhysPolA.128.B-184