Neural Computing and Applications

, Volume 31, Supplement 1, pp 409–424 | Cite as

Self-compacting concrete strength prediction using surrogate models

  • Panagiotis G. AsterisEmail author
  • Konstantinos G. Kolovos
Original Article


Despite the extensive use of self-compacting concrete in constructions over the last decades, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength based on its mix components. Τhis limitation is due to the highly nonlinear relation between the self-compacting concrete’s compressive strength and the mixed components. In this paper, the application of artificial neural networks for predicting the mechanical characteristics of self-compacting concrete has been investigated. Specifically, surrogate models (such as artificial neural network models and a new proposed normalization method) have been used for predicting the 28-day compressive strength of admixture-based self-compacting concrete (based on experimental data available in the literature). The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of self-compacting concrete in a reliable and robust manner. Furthermore, the proposed formula for the normalization of data has been proven effective and robust compared to available ones.


Artificial neural networks Back propagation neural networks Compressive strength Self-compacting concrete 



The research was performed within the framework of the Master’s Program in Applied Computational Structural Engineering (ACSE), which has been partially financed by the Research Committee of the School of Pedagogical and Technological Education, Athens, Greece. The authors would like to express their gratitude to MSc students Mrs. M.G. Douvika, Mr. K. Roinos and Mr. A.K. Tsaris and to the undergraduate students Mr. N. Margaris and Mr. D. Georgakopoulos for their assistance on the computational implementation of the ANN models.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Panagiotis G. Asteris
    • 1
    Email author
  • Konstantinos G. Kolovos
    • 2
  1. 1.Computational Mechanics LaboratorySchool of Pedagogical and Technological EducationHeraklionGreece
  2. 2.Department of Physical Sciences and ApplicationsHellenic Army AcademyVariGreece

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