Miguel Abambres and I worked on the development of an ANN-based expression for the fatigue strenght of concrete under compression – using Miguel’s algorithm and my database of fatigue tests. I’m quite pleased that the resulting model only takes 3 input values, so it’s as easy to use as most code equations, yet much more accurate.
You can access the paper (Open Access) which is published in Materials here.
The abstract is:
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 study the reduced capacity as a function of a given number of cycles by means of artificial neural networks. We used an input database with 203 datapoints gathered from the literature. To find the optimal neural network, 14 features of neural networks were studied and varied, resulting in the optimal neural net. This proposed model resulted in a maximum relative error of 5.1% and a mean relative error of 1.2% for the 203 datapoints. The proposed model resulted in a better prediction (mean tested to predicted value = 1.00 with a coefficient of variation 1.7%) as compared to the existing code expressions. The model we developed can thus be used for the design and the assessment of concrete structures and provides a more accurate assessment and design than the existing methods.