Computers and Concrete

  • 제19권6호
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  • Pages.651-658
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  • 2017
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
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DOI QR Code

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

  • 투고 : 2016.01.16
  • 심사 : 2017.02.15
  • 발행 : 2017.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

참고문헌

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피인용 문헌

  1. A new formulation for strength characteristics of steel slag aggregate concrete using an artificial intelligence-based approach vol.27, pp.4, 2017, https://doi.org/10.12989/cac.2021.27.4.333