ANN-based Fatigue Strength of Concrete Under Compression
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When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we predict the reduced capacity as function of a given number of cycles by means of artificial neural networks (ANN). A 203-point experimental dataset gathered from the literature was used. The proposed ANN model results in a maximum relative error of 5.1% and a mean counterpart of 1.2% for the whole dataset. It’s shown that the proposed analytical model outperforms the existing design code expressions.