  Icephobic surfaces inspired by superhydrophobic surfaces offer a passive
solution to the problem of icing. However, modeling icephobicity is challenging
because some material features that aid superhydrophobicity can adversely
affect the icephobic performance. This study presents a new approach based on
artificial neural networks to model icephobicity. Artificial neural network
models were developed to predict the icephobic performance of concrete. The
models were trained on experimental data to predict the surface ice adhesion
strength and the coefficient of restitution (COR) of water droplet bouncing off
the surface under freezing conditions. The material and coating compositions,
and environmental condition were used as the models' input variables. A
multilayer perceptron was trained to predict COR with a root mean squared error
of 0.08, and a 90% confidence interval of [0.042, 0.151]. The model had a
coefficient of determination of 0.92 after deployment. Since ice adhesion
strength varied over a wide range of values for the samples, a mixture density
network was model was developed to learn the underlying relationship in the
multimodal data. Coefficient of determination for the model was 0.96. The
relative importance of the input variables in icephobic performance were
calculated using permutation importance. The developed models will be
beneficial to optimize icephobicity of concrete.
