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Concrete compressive strength using artificial neural networks

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Abstract

The non-destructive testing of concrete structures with methods such as ultrasonic pulse velocity and Schmidt rebound hammer test is of utmost technical importance. Non-destructive testing methods do not require sampling, and they are simple, fast to perform, and efficient. However, these methods result in large dispersion of the values they estimate, with significant deviation from the actual (experimental) values of compressive strength. In this paper, the application of artificial neural networks (ANNs) for predicting the compressive strength of concrete in existing structures has been investigated. ANNs have been systematically used for predicting the compressive strength of concrete, utilizing both the ultrasonic pulse velocity and the Schmidt rebound hammer experimental results, which are available in the literature. The comparison of the ANN-derived results with the experimental findings, which are in very good agreement, demonstrates the ability of ANNs to estimate the compressive strength of concrete in a reliable and robust manner. Thus, the (quantitative) values of weights for the proposed neural network model are provided, so that the proposed model can be readily implemented in a spreadsheet and accessible to everyone interested in the procedure of simulation.

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Abbreviations

\( B \) :

Vector of bias values

\( f_{\text{c}} \) :

Compressive strength of concrete

\( {\text{IW}} \) :

Matrix of weights values for input layer

\( {\text{LW}} \) :

Matrix of weights values for hidden layer

\( R \) :

Rebound hammer

\( V_{\text{p}} \) :

Ultrasonic pulse velocity

ANNs:

Artificial neural networks

BP:

Back propagation

RH:

Rebound hammer

UPV:

Ultrasonic pulse velocity

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  1. Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121, Heraklion, Athens, Greece

    Panagiotis G. Asteris & Vaseilios G. Mokos

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  1. Panagiotis G. Asteris
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Asteris, P.G., Mokos, V.G. Concrete compressive strength using artificial neural networks. Neural Comput & Applic 32, 11807–11826 (2020). https://doi.org/10.1007/s00521-019-04663-2

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