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


Erdem R. T. , KANTAR E., Gucuyen E., ANIL Ö.

COMPUTERS AND CONCRETE, vol.12, no.5, pp.613-625, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 12 Issue: 5
  • Publication Date: 2013
  • Doi Number: 10.12989/cac.2013.12.5.613
  • Journal Name: COMPUTERS AND CONCRETE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.613-625
  • Keywords: compression strength, polypropylene fibre, artificial neural networks, PREDICTION
  • Gazi University Affiliated: Yes

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.