Computers and Concrete

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Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars

  • Asteris, Panagiotis G. (Computational Mechanics Laboratory, School of Pedagogical and Technological Education) ;
  • Apostolopoulou, Maria (Laboratory of Materials Science and Engineering, School of Chemical Engineering, National Technical University of Athens, Zografou Campus) ;
  • Skentou, Athanasia D. (Computational Mechanics Laboratory, School of Pedagogical and Technological Education) ;
  • Moropoulou, Antonia (Laboratory of Materials Science and Engineering, School of Chemical Engineering, National Technical University of Athens, Zografou Campus)
  • Received : 2019.03.26
  • Accepted : 2019.09.05
  • Published : 2019.10.25
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Abstract

Despite the extensive use of mortar materials in constructions over the last decades, there is not yet a robust quantitative method, available in the literature, which can reliably predict mortar strength based on its mix components. This limitation is due to the highly nonlinear relation between the mortar's compressive strength and the mixed components. In this paper, the application of artificial neural networks for predicting the compressive strength of mortars has been investigated. Specifically, surrogate models (such as artificial neural network models) have been used for the prediction of the compressive strength of mortars (based on experimental data available in the literature). Furthermore, compressive strength maps are presented for the first time, aiming to facilitate mortar mix design. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of mortars in a reliable and robust manner.

Keywords

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