PSI - Issue 79

Available online at www.sciencedirect.com

ScienceDirect

Procedia Structural Integrity 79 (2026) 485–492

© 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of IGF28 - MedFract3 organizers Keywords: machine learning approaches; artificial neural networks; damage evolution; multiscale models; periodic homogenization; bioinspired composites. Abstract Nowadays, accurately predicting how advanced bioinspired composite structures fail under various loading conditions remains a complex task. This is primarily because several different damage mechanisms can occur simultaneously at different scales. Among the existing numerical approaches for predicting failure in such structures, multiscale models are the most promising by virtue of their optimal compromise between accuracy and efficiency. This work introduces a cost-effective, machine learning based multiscale approach for studying how damage evolves in bioinspired periodic composite materials under general loading conditions. A crucial component of this approach is a data-driven surrogate macroscale damage evolution model. This model is developed by performing several nonlinear homogenization steps on the same bioinspired microstructure to be used for training a deep neural network (DNN). The reliability of this multiscale model is assessed by comparing its numerical predictions with the results coming from classical micromechanical approaches, with reference to both proportional and nonproportional loadings. 28th International Conference on Fracture and Structural Integrity - 3rd Mediterranean Conference on Fracture and Structural Integrity Damage evolution analysis in bioinspired composite structures by using a machine learning-based multiscale modeling approach Lorenzo Leonetti a, *, Domenico Ammendolea a , Fabrizio Greco a , Paolo Lonetti a , Paolo Nevone Blasi a , Girolamo Sgambitterra a a Department of Civil Engineering, University of Calabria, Via P. Bucci, Cubo 39B, 87036, Rende, Italy

* Corresponding author. Tel.: +39-0984-496605. E-mail address: lorenzo.leonetti@unical.it

2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of IGF28 - MedFract3 organizers 10.1016/j.prostr.2025.12.360