  Constitutive models for concrete based on the microplane concept have
repeatedly proven their ability to well-reproduce its non-linear response on
material as well as structural scales. The major obstacle to a routine
application of this class of models is, however, the calibration of
microplane-related constants from macroscopic data. The goal of this paper is
two-fold: (i) to introduce the basic ingredients of a robust inverse procedure
for the determination of dominant parameters of the M4 model proposed by Bazant
and co-workers based on cascade Artificial Neural Networks trained by
Evolutionary Algorithm and (ii) to validate the proposed methodology against a
representative set of experimental data. The obtained results demonstrate that
the soft computing-based method is capable of delivering the searched response
with an accuracy comparable to the values obtained by expert users.
