Computers and Concrete

  • 제22권4호
  • /
  • Pages.419-437
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  • 2018
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Predicting the compressive strength of self-compacting concrete containing fly ash using a hybrid artificial intelligence method

  • Golafshani, Emadaldin M. (Department of Civil Engineering, Architecture and Art, Science and Research Branch, Islamic Azad University) ;
  • Pazouki, Gholamreza (Department of Civil Engineering, Architecture and Art, Science and Research Branch, Islamic Azad University)
  • 투고 : 2018.04.29
  • 심사 : 2018.10.11
  • 발행 : 2018.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

The compressive strength of self-compacting concrete (SCC) containing fly ash (FA) is highly related to its constituents. The principal purpose of this paper is to investigate the efficiency of hybrid fuzzy radial basis function neural network with biogeography-based optimization (FRBFNN-BBO) for predicting the compressive strength of SCC containing FA based on its mix design i.e., cement, fly ash, water, fine aggregate, coarse aggregate, superplasticizer, and age. In this regard, biogeography-based optimization (BBO) is applied for the optimal design of fuzzy radial basis function neural network (FRBFNN) and the proposed model, implemented in a MATLAB environment, is constructed, trained and tested using 338 available sets of data obtained from 24 different published literature sources. Moreover, the artificial neural network and three types of radial basis function neural network models are applied to compare the efficiency of the proposed model. The statistical analysis results strongly showed that the proposed FRBFNN-BBO model has good performance in desirable accuracy for predicting the compressive strength of SCC with fly ash.

키워드

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피인용 문헌

  1. Prediction of concrete spall damage under blast: Neural approach with synthetic data vol.26, pp.6, 2018, https://doi.org/10.12989/cac.2020.26.6.533