Prediction of high performance concrete strength using Genetic Programming with geometric semantic genetic operators

Research output: Contribution to journalArticlepeer-review

62 Citations (Scopus)

Abstract

Concrete is a composite construction material made primarily with aggregate, cement, and water. In addition to the basic ingredients used in conventional concrete, high-performance concrete incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, high-performance concrete is a highly complex material and modeling its behavior represents a difficult task. In this paper, we propose an intelligent system based on Genetic Programming for the prediction of high-performance concrete strength. The system we propose is called Geometric Semantic Genetic Programming, and it is based on recently defined geometric semantic genetic operators for Genetic Programming. Experimental results show the suitability of the proposed system for the prediction of concrete strength. In particular, the new method provides significantly better results than the ones produced by standard Genetic Programming and other machine learning methods, both on training and on out-of-sample data.

Original languageEnglish
Pages (from-to)6856-6862
Number of pages7
JournalExpert Systems with Applications
Volume40
Issue number17
DOIs
Publication statusPublished - 24 Jul 2013

Keywords

  • Artificial intelligence
  • Genetic Programming
  • Geometric operators
  • High performance concrete
  • Semantics
  • Strength prediction

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