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

TL;DR

This paper presents a robust inverse method using soft computing techniques to calibrate microplane M4 model parameters for concrete, validated against experimental data, improving routine application of the model.

Contribution

It introduces a novel calibration methodology combining neural networks and evolutionary algorithms for the microplane M4 model.

Findings

Method achieves calibration accuracy comparable to expert users.

Validated against experimental data with successful results.

Provides a practical approach for routine model calibration.

Abstract

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.