Browse > Article
http://dx.doi.org/10.12989/cac.2013.12.5.613

Estimation of compression strength of polypropylene fibre reinforced concrete using artificial neural networks  

Erdem, R. Tugrul (Civil Engineering Department, Celal Bayar University)
Kantar, Erkan (Civil Engineering Department, Celal Bayar University)
Gucuyen, Engin (Civil Engineering Department, Celal Bayar University)
Anil, Ozgur (Civil Engineering Department, Gazi University)
Publication Information
Computers and Concrete / v.12, no.5, 2013 , pp. 613-625 More about this Journal
Abstract
In this study, Artificial Neural Networks (ANN) analysis is used to predict the compression strength of polypropylene fibre mixed concrete. Polypropylene fibre admixture increases the compression strength of concrete to a certain extent according to mix proportion. This proportion and homogenous distribution are important parameters on compression strength. Determination of compression strength of fibre mixed concrete is significant due to the veridicality of capacity calculations. Plenty of experiments shall be completed to state the compression strength of concrete which have different fibre admixture. In each case, it is known that performing the laboratory experiments is costly and time-consuming. Therefore, ANN analysis is used to predict the 7 and 28 days of compression strength values. For this purpose, 156 test specimens are produced that have 26 different types of fibre admixture. While the results of 120 specimens are used for training process, 36 of them are separated for test process in ANN analysis to determine the validity of experimental results. Finally, it is seen that ANN analysis predicts the compression strength of concrete successfully.
Keywords
compression strength; polypropylene fibre; artificial neural networks;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Atici, U. (2011), "Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network", Exp. Syst. Appl., 38, 9609-9618.   DOI   ScienceOn
2 Ashrafi, H.R., Jalal, M. and Garmsiri, K. (2010), "Prediction of load?displacement curve of concrete reinforced by composite fibers (steel and polymeric) using artificial neural network", Exp. Syst. Appl., 37, 7663-7668.   DOI   ScienceOn
3 Guang, N.H. and Zong, W.J. (2000) "Prediction of compressive strength of concrete by neural networks", Cement Concrete Res., 30, 1245-1250.   DOI   ScienceOn
4 Kim, J.L., Kim, D.K., Feng, M.Q. and Yazdani, F. (2004), "Application of neural network for estimation of concrete strength", J. Mater. Civil Eng., 257-264.
5 Lee, S.C. (2003), Prediction of concrete strength using artificial neural Networks, Eng. Struct., 25, 849-857.   DOI   ScienceOn
6 Onal, O. and Ozturk, A.U. (2010), "Artificial neural network application on microstructure?compressive strength relationship of cement mortar", Adv. Eng. Softw., 41, 165-169.   DOI   ScienceOn
7 Prasad, B.K.R., Eskandari, H. and Reddy, B.V.V. (2009), "Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN", Construct. Build. Mater., 23, 117-128.   DOI   ScienceOn
8 Słonski, M. (2010), "A comparison of model selection methods for compressive strength prediction of high-performance concrete using neural networks", Comput. Struct., 88, 1248-1253.   DOI   ScienceOn
9 Siddique, R., Aggarwal, P. and Aggarwal, Y. (2011), "Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks", Adv. Eng. Softw., 42, 780-786.   DOI   ScienceOn
10 Yeh, I.C. (1998), "Modeling concrete strength with augment-neuron networks", J. Mater. Civil Eng., 10(4), 263-208.   DOI   ScienceOn
11 Zurada, J.M. (1992), Introduction to Artificial Neural Networks, West Publishing.com.