Open AccessM5' and Mars Based Prediction Models for Properties of Self-compacting Concrete Containing Fly Ash
Author(s) -
A. Kaveh,
Taha Bakhshpoori,
Seyed Mahmood HamzeZiabari
Publication year - 2017
Publication title -
periodica polytechnica. civil engineering/periodica polytechnica. civil engineering (online)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.406
H-Index - 19
eISSN - 1587-3773
pISSN - 0553-6626
DOI - 10.3311/ppci.10799
Subject(s) - multivariate adaptive regression splines , fly ash , mars exploration program , cement , compressive strength , rheology , geotechnical engineering , multivariate statistics , computer science , algorithm , materials science , engineering , linear regression , machine learning , bayesian multivariate linear regression , composite material , physics , astronomy
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.