M5' and Mars Based Prediction Models for Properties of Self-compacting Concrete Containing Fly Ash

  • Ali Kaveh Iran University of Science and Technology, Iran
  • Taha Bakhshpoori
  • Seyed Mahmood Hamze-Ziabari

Abstract

The main purpose of this paper is to predict the properties (mechanical and rheological) of the self-compacting concrete (SCC) containing fly ash as cement replacement by using two decision tree algorithms: M5′ and Multivariate adaptive regression splines (Mars). The M5′ algorithm as a rule based method is used to develop new practical equations while the MARS algorithm besides its high predictive ability is used to determine the most important parameters. To achieve this purpose, a data set containing 114 data points related to effective parameters affect on SSC properties is used. A gamma test is employed to determine the most effective parameters in prediction of the compressive strength at 28 days, the V-funnel time, the slump flow, and the L-box ratio of SCC. The results from this study suggests that tree based models perform remarkably well in predicting the properties of the self-compacting concrete containing fly ash as cement replacement. 

Keywords

self-compacting concrete (SCC), fly ash, M5′, MARS
Published in Onlinefirst
13-07-2017
How to Cite
KAVEH, Ali; BAKHSHPOORI, Taha; HAMZE-ZIABARI, Seyed Mahmood. M5' and Mars Based Prediction Models for Properties of Self-compacting Concrete Containing Fly Ash. Periodica Polytechnica Civil Engineering, [S.l.], 2017. ISSN 1587-3773. Available at: <https://pp.bme.hu/ci/article/view/10799>. Date accessed: 22 nov. 2017. doi: https://doi.org/10.3311/PPci.10799.
Section
Research Article