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04-01-2024

A Hybrid Parameter Homogenization Workflow for Assessing the Mechanical Behavior of a Steel Fiber-Reinforced Concrete

Authors: M. Congro, F. L. G. Pereira, L. M. S. Souza, D. Roehl

Published in: Mechanics of Composite Materials | Issue 6/2024

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Abstract

This article introduces a hybrid workflow that leverages artificial neural networks (ANNs) to estimate the homogenized mechanical properties of steel fiber-reinforced concrete. The methodology involves a sensitivity analysis using Design of Experiments (DOE) to identify key input parameters and develops three ANNs to predict Young’s modulus, tensile strength, and fracture energy. These parameters are then input into finite-element simulations to validate the global mechanical behavior of the composite. The workflow demonstrates excellent agreement with experimental data, highlighting the potential to significantly reduce computational costs and enhance the accuracy of material modeling in the early stages of product development. The integration of ANNs with finite-element analysis offers a promising approach to simulate complex materials like steel fiber-reinforced concrete, making it a valuable read for professionals seeking innovative solutions in materials science and civil engineering.

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Metadata
Title
A Hybrid Parameter Homogenization Workflow for Assessing the Mechanical Behavior of a Steel Fiber-Reinforced Concrete
Authors
M. Congro
F. L. G. Pereira
L. M. S. Souza
D. Roehl
Publication date
04-01-2024
Publisher
Springer US
Published in
Mechanics of Composite Materials / Issue 6/2024
Print ISSN: 0191-5665
Electronic ISSN: 1573-8922
DOI
https://doi.org/10.1007/s11029-023-10163-1

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