An evolutionary system for the prediction of high performance concrete strength based on semantic genetic programming

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2 Citations (Scopus)

Abstract

High-performance concrete, besides aggregate, cement, and water, incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, it is a highly complex material and modeling its behavior represents a difficult task. This paper presents an evolutionary system for the prediction of high performance concrete strength. The proposed framework blends a recently developed version of genetic programming with a local search method. The resulting system enables us to build a model that produces an accurate estimation of the considered parameter. Experimental results show the suitability of the proposed system for the prediction of concrete strength. The proposed method produces a lower error with respect to the state-of-the art technique. The paper provides two contributions: from the point of view of the high performance concrete strength prediction, a system able to outperform existing state-of-the-art techniques is defined; from the machine learning perspective, this case study shows that including a local searcher in the geometric semantic genetic programming system can speed up the convergence of the search process.

Original languageEnglish
Pages (from-to)651-658
Number of pages8
JournalComputers and Concrete
Volume19
Issue number6
DOIs
Publication statusPublished - 1 Jun 2017

Keywords

  • Concrete strength
  • Genetic programming
  • High performance concrete
  • Local search
  • Semantics

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