Estimating strength of rubberized concrete using evolutionary multivariate adaptive regression splines

Min Yuan Cheng, Minh Tu Cao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

This study proposes an artificial intelligence (AI) model to predict the compressive strength and splitting tensile strength of rubberized concrete. This Evolutionary Multivariate Adaptive Regression Splines (EMARS) model is a hybrid of the Multivariate Adaptive Regression Splines (MARS) and Artificial Bee Colony (ABC) within which MARS addresses learning and curve fitting and ABC implements optimization to determine the fittest parameter settings with minimal prediction error. K-fold cross validation was utilized to compare EMARS performance against four other benchmark data mining techniques including MARS, Back-propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Genetic Programming (GP). Comparison results showed EMARS to be the best model for predicting rubberized concrete strength and study results demonstrated EMARS as a reliable tool for civil engineers in the concrete construction industry.

Original languageEnglish
Pages (from-to)711-720
Number of pages10
JournalJournal of Civil Engineering and Management
Volume22
Issue number5
DOIs
StatePublished - 2016

Keywords

  • artificial bee colony
  • artificial intelligence
  • concrete strength
  • multivariate adaptive regression splines
  • rubberized concrete
  • silica fume

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