Aug 2026· Building Materials and Engineering Structures· 0 citations· 36 references
TL;DR
This study examines the application of ML techniques to composite materials, particularly for predicting fracture toughness, characterizing damage, and optimizing mechanical properties and reveals significant relationships between fracture toughness and important input parameters.
Abstract
Machine learning (ML) has become an important technology in the field of composite materials, offering efficientmethodsformaterialcharacterization, damageassessment, propertyprediction, anddesignoptimization. Conventional experimental techniques and physics-based simulations for predicting the complex behavior of composites can require considerable time, cost, and computational resources. In contrast, ML provides a data-driven approach capable of identifying hidden patterns, establishing relationships between variables, and generating accurate predictions. Algorithms such as Support Vector Regression (SVR), Random Forest Regression, and Linear Regression can process information related to material composition, filler characteristics, curing conditions, and environmental factors. This study examines the application of ML techniques to composite materials, particularly for predicting fracture toughness, characterizing damage, and optimizing mechanical properties. Correlation analysis reveals significant relationships between fracture toughness and important input parameters, demonstrating the usefulness of data-driven approaches in material development. Among the investigated techniques, Random Forest Regression demonstrates strong predictive capability and effectively represents complex material behavior. However, challenges including limited data availability, model interpretability, and generalization remain. Reliable ML performance depends on high-quality datasets, suitable feature selection, and effective model tuning. Overfitting can also affect prediction accuracy, making cross-validation and hyperparameter optimization essential. Integrating ML into composite material research can reduce experimental requirements, lower development costs, improve manufacturing efficiency, and accelerate the development of high-performance materials. Future research should emphasize larger datasets, explainable ML, and hybrid approaches combining ML with physics-based simulations.
The paper presents an approach to constructing predictive models for the physical and mechanical properties of elastomeric composites using machine learning methods. The relevance of the study is driven by the need to accelerate the development of new materials and reduce the labor intensity of full-scale experiments. An automated machine learning algorithm is proposed, encompassing stages of input data unification, feature space formation, and comparative analysis of regression models (Random Forest, Gradient Boosted Decision Trees, Gradient Tree-Boosting Tweedie, Poisson Regression, Light Gradient Boosting Machine и Stochastic Dual Coordinate Ascent). During experimental validation on datasets containing formulation data with varying content of sulfur, natural rubber (NR), and zinc oxide, predictions were made for theoretical density, Karrer plasticity, brittleness temperature, and curing temperature. It was established that ensemble methods demonstrate the highest predictive capability; however, model accuracy significantly depends on sample representativeness. Intervals of the studied parameters (particularly the 140–150 °C range for curing temperature) characterized by increased prediction uncertainty were identified, requiring additional algorithm calibration. The obtained results confirm the effectiveness of the proposed approach for formulation optimization and identification of hidden dependencies in the “composition-property” system.
M. Maslova, V. Kablov, A. Rybanov· Computational nanotechnology· 0 citations
Accurate prediction of concrete compressive strength is essential for effective mix design, quality control, and structural performance assessment. Conventional empirical models often exhibit limited accuracy due to the complex and nonlinear interactions among concrete constituents.
This study investigates the applicability of several machine learning models for predicting the compressive strength of concrete using a publicly available experimental dataset comprising 1030 concrete mixtures. Linear regression was adopted as a baseline model and compared with support vector regression, random forest regression, and artificial neural networks.
The performance of machine learning models was meticulously assessed using the coefficient of determination, root mean square error, and mean absolute error. Additionally, the models underwent five-fold cross-validation to evaluate their robustness and generalization capabilities. The results unambiguously demonstrate that machine learning models significantly outperform linear regression models.
Cross-validation results confirm the stability and reliability of the developed models. Feature importance analysis reveals that curing age and cement content are the most influential parameters affecting compressive strength, followed by water content, which is consistent with established concrete material behavior. The findings demonstrate that machine learning models, particularly random forest regression, can serve as effective supporting tools for preliminary concrete mix design and performance evaluation.
S. Rouabah· ITEGAM- Journal of Engineeri...· 0 citations
Accurate prediction and optimization of polymer composite properties is of paramount importance in the design of these lightweight, durable, and sustainable materials within renewable energy technologies. This work will provide a holistic machine learning-assisted framework that unites materials informatics with domain-specific features and state-of-the-art ML methodologies in the prediction of the mechanical properties of polymer composites, such as tensile strength. This includes embedding several ensemble models, including Random Forest and Gradient Boosting, kernel methods such as SVR, neural networks, graph-based approaches, while it applies principled hyperparameter tuning and uncertainty quantification, along with model-interpretability tools such as SHAP and systematic ablation to identify the most important material and processing factors. The proposed methodology is demonstrated with curated, publicly available datasets, and we discuss means for synthetic data augmentation, cross-validation protocols, assessment of model robustness, and best practices in reporting results in a reproducible way. The results show that data-driven models reduce prediction error and speed up the processes of materials selection in view of Net Zero targets, given limited available experimental iterations; this informs intelligent lightweighting of renewable energy components. Furthermore, the study highlights the capability of machine learning models to capture complex nonlinear relationships between composition, processing parameters, and mechanical response that are difficult to address using conventional trial-and-error approaches. By reducing reliance on extensive experimental campaigns, the proposed framework supports faster design cycles and more efficient utilization of material and energy resources in renewable energy applications.
With the growing demand for advanced polymer composites, intelligent prediction methods offer an efficient route to optimize material performance while minimizing experimental effort and cost. This study focuses on fly ash and
Azadirachta indica
reinforced polymer composites, where regression-based machine learning models were employed to predict key mechanical properties, specifically flexural and impact behaviors. Several models, including LASSO, Ridge, Elastic Net, Bayesian Ridge with Automatic Relevance Determination, and Huber regression, were developed and evaluated. Without feature engineering, LASSO and ARD demonstrated strong predictive performance, while Ridge and Huber showed weaker generalization. Incorporating feature engineering significantly improved prediction accuracy and robustness, particularly for flexural and impact behavior. The proposed ML framework demonstrates the potential of intelligent prediction in guiding composite formulation, thereby reducing experimental trials and accelerating the development of high-performance polymer composites.
Jitender Yadav, P. Ramkumar, A. K. Parwani et al.· Journal of reinforced plasti...· 0 citations
This article presents how ML approaches may speed up material discovery, reduce trial-and-error, and enable individualized design solutions for 3D-printed polymers across a variety of contexts.
N. Senthilkumar, S. Gopalakrishnan, S. Gopinath et al.· Interactions· 0 citations
Machine learning (ML) provides new opportunities to model the nonlinear relationships among composition, processing, microstructure, defects, properties, and in-service degradation of structural steels. This structured critical review examines ML applications to materials and process design, microstructural characterization, mechanical-property prediction, corrosion, fire and elevated-temperature performance, fatigue, fracture, and remaining-life assessment. Literature published up to 31 July 2026 was searched primarily through the Web of Science Core Collection and Scopus. A total of 110 publications were retained based on their relevance to structural steels, transparency of data and modeling procedures, and availability of information on validation or engineering applicability. The reviewed studies show that model suitability depends strongly on data modality, sample independence, feature representation, and validation strategy rather than on algorithm family alone. ML has progressed from property prediction toward process optimization, inverse materials design, environmental degradation assessment, and fatigue- and crack-related prognostics. However, independent cross-manufacturer, cross-laboratory, production-scale, and field validation remains limited, while uncertainty quantification and applicability-domain assessment are still inconsistently reported. These limitations are particularly important for corrosion, fire, fatigue, and remaining-life applications, where internally validated models should not be interpreted as substitutes for established physical models or design provisions. Future research should prioritize standardized multimodal data, physics-informed and uncertainty-aware modeling, prospective validation, and rigorously evaluated closed-loop monitoring and digital-twin frameworks for structural-steel life-cycle management.
Guomin Wei, Minghe Li, B. Cui et al.· Materials· 0 citations