Soft computing-based calibration of microplane M4 model parameters: Methodology and validation

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.