research-article

Determination of the resistance characteristics of self-compacting concrete samples by artificial neural network

Authors:

Sakir Tasdemir,

Ismail Saritas,

Mehmet Kamanli,

M. Tolga Cogurcu,

M. Yasar KaltakciAuthors Info & Claims

CompSysTech '08: Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing

Article No.: 40, Pages IIIB.5 - 1

https://doi.org/10.1145/1500879.1500924

Published: 12 June 2008 Publication History

CompSysTech '08: Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing

Determination of the resistance characteristics of self-compacting concrete samples by artificial neural network

Pages IIIB.5 - 1

Abstract
References

Abstract

In this study, in order to determine the resistance characteristics of self-compacting concrete (SCC) samples, experiments were done in the Konya Cement Factory, Ready-mix Concrete Establishment. Four different mixture proportions were chosen in the experimental study. 24 samples of the 4 mixtures were selected in order to set the cube compression strength. For each mixture, these 24 samples were broken down within 28 days and the characteristics of cube compression strength were obtained. After 28 days, compression strength average was found to be 50.0300 MPa. A model of Artificial Neural Network (ANN) was designed for this study and the results were obtained in this model of ANN. Both experimental and ANN data was analyzed with SPSS statistical packet software. The result of statistical analysis (p=0.9972) has been done in 95% of confidence interval. It has been seen that the ANN can be used as reliable modelling method for similar studies.

References

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Cited By

Tomasiello S(2011)A functional network to predict fresh and hardened properties of self‐compacting concretesInternational Journal for Numerical Methods in Biomedical Engineering10.1002/cnm.133327:6(840-847)Online publication date: 25-May-2011
https://doi.org/10.1002/cnm.1333

Determination of the resistance characteristics of self-compacting concrete samples by artificial neural network
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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cover image ACM Other conferences

CompSysTech '08: Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing

June 2008

598 pages

ISBN:9789549641523

DOI:10.1145/1500879

Editors:
Boris Rachev
Technical University of Varna, Bulgaria
,
Anger Smrikarov
University of Rousse, Bulgaria

Copyright © 2008 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2008

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Cited By

Tomasiello S(2011)A functional network to predict fresh and hardened properties of self‐compacting concretesInternational Journal for Numerical Methods in Biomedical Engineering10.1002/cnm.133327:6(840-847)Online publication date: 25-May-2011
https://doi.org/10.1002/cnm.1333

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