Geomechanics and Engineering

  • 제25권4호
  • /
  • Pages.317-330
  • /
  • 2021
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  • 2005-307X(pISSN)
  • /
  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Predicting the unconfined compressive strength of granite using only two non-destructive test indexes

  • Armaghani, Danial J. (Department of Civil Engineering, Faculty of Engineering, University of Malaya) ;
  • Mamou, Anna (Computational Mechanics Laboratory, School of Pedagogical and Technological Education) ;
  • Maraveas, Chrysanthos (Department of Civil Engineering, University of Patras) ;
  • Roussis, Panayiotis C. (Department of Civil and Environmental Engineering, University of Cyprus) ;
  • Siorikis, Vassilis G. (Computational Mechanics Laboratory, School of Pedagogical and Technological Education) ;
  • Skentou, Athanasia D. (Computational Mechanics Laboratory, School of Pedagogical and Technological Education) ;
  • Asteris, Panagiotis G. (Computational Mechanics Laboratory, School of Pedagogical and Technological Education)
  • 투고 : 2021.03.24
  • 심사 : 2021.05.12
  • 발행 : 2021.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

This paper reports the results of advanced data analysis involving artificial neural networks for the prediction of the unconfined compressive strength of granite using only two non-destructive test indexes. A data-independent site-independent unbiased database comprising 182 datasets from non-destructive tests reported in the literature was compiled and used to train and develop artificial neural networks for the prediction of the unconfined compressive strength of granite. The results show that the optimum artificial network developed in this research predicts the unconfined compressive strength of weak to very strong granites (20.3-198.15 MPa) with less than ±20% deviation from the experimental data for 70% of the specimen and significantly outperforms a number of available models available in the literature. The results also raise interesting questions with regards to the suitability of the Pearson correlation coefficient in assessing the prediction accuracy of models.

키워드

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