A methodology to generate design allowables of composite laminates using machine learning

C. Furtado*, R. P. Tavares, L.P. Gomes Pereira, M. Salgado, F. Otero, G. Catalanotti, A. Arteiro, M. A. Bessa, P. P. Camanho

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

32 Citations (Scopus)


This work represents the first step towards the application of machine learning techniques in the prediction of statistical design allowables of composite laminates. Building on data generated analytically, four machine algorithms (XGBoost, Random Forests, Gaussian Processes and Artificial Neural Networks) are used to predict the notched strength of composite laminates and their statistical distribution, associated to the uncertainty related to the material properties and geometrical features. This work focuses not only on the so-called Legacy Quad Laminates (0°/90°/±45°), typically used in the design of composite aerostructures, but also on the newer concept of double-double (or double-angle ply) laminates. Very good representations of the design space, translating in low generalization relative errors of around ±10%, and very accurate representations of the distributions of notched strengths around single design points and corresponding B-basis allowables are obtained. All machine learning algorithms, with the exception of the Random Forests, show very good performances, with Gaussian Processes outperforming the others for very small number of data points while Artificial Neural Networks have better performance for larger training sets. This work serves as basis for the prediction of first-ply failure, ultimate strength and failure mode of composite specimens based on non-linear finite element simulations, providing further reduction of the computational time required to virtually obtain the design allowables for composite laminates.

Original languageEnglish
Article number111095
Number of pages14
JournalInternational Journal of Solids and Structures
Publication statusPublished - 2021


  • Design allowables
  • Fracture mechanics
  • Machine learning
  • Polymer-matrix composites (PMCs)

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