Authors: Shifa Burhan | Adnan Adhab K. Al-Saeedi | Abbas Jalal Kaishesh | Dhiyaa Salih Hammad | Anmar Dulaimi | Luís Filipe Almeida Bernardo | Jorge Miguel de Almeida Andrade
Source: Journal of Composites Science (article, 2025-11-09)
Field: Materials Science — Polymers and Plastics

Predicting Stress–Strain Behavior of Silica–Epoxy Nanocomposites Using Random Forest Regression The accurate prediction of the mechanical behaviour of silica–epoxy nanocomposites is essential for advancing their application in high-performance industries, including aerospace, automotive, and structural engineering. Conventional experimental characterization methods are often time-consuming and costly, highlighting the need for efficrelianceient computational alternatives. This study proposes a machine learning based on Random Forest Regression to predict the stress–strain behaviour of silica–epoxy nanocomposites with high accuracy. The model employs two independent and physically meaningful input parameters—SiO2 nanoparticle concentration (wt%) and strain—to predict stress, thereby capturing the true constitutive relationship of the material. The model was trained and validated on an extensive experimental dataset of 7422 observations across five compositions (0–4 wt% SiO2), obtained from systematic tensile testing following the ASTM D638 standard. Rigorous stratified 10-fold cross-validation confirmed excellent generalization (mean R2 = 0.9977 ± 0.0023) with minimal overfitting (training–validation gap &lt; 0.005). The performance of the test set (R2 = 0.9948, mean absolute error (MAE) = 0.0404 MPa) surpasses recent literature benchmarks by nearly 5%, establishing state-of-the-art accuracy in nanocomposite property prediction. Error analysis revealed stable prediction accuracy throughout the elastic and plastic regimes (error variance &lt; 0.004 MPa2 for strain), with a physically consistent increase in error near failure due to complex damage mechanisms. Feature importance analysis indicated that strain and SiO2 concentration contributed 78.4% and 21.6%, respectively, to predictive accuracy. This is consistent with constitutive modelling principles, in which deformation state primarily determines stress magnitude, while composition modulates the functional relationship. Mechanical property extraction from experimental curves showed optimal performance at 2–3 wt% SiO2, yielding balanced enhancements in tensile strength (+1–2%) and failure strain (+36–64%) relative to neat epoxy. The validated framework reduces material development time by 65–80% and cost by 60–75% compared with conventional trial-and-error methods, offering a robust, data-driven tool for the efficient design and optimization of silica–epoxy nanocomposites. A comprehensive discussion of limitations and applicability boundaries ensures the framework’s responsible and reliable deployment in engineering practice. Overfitting ; Random forest ; Generalization ; Nanocomposite ; Regression ; Set (abstract data type) ; Variance (accounting) ; Test set ; Property (philosophy) ; Feature (linguistics) Polymer Nanocomposites and Properties ; Machine Learning in Materials Science ; Mechanical Behavior of Composites