Machine Learning-Based Static Performance Prediction of Bonded Structural Patch Repairs
TL;DR
Overall, this study provides a novel, data-efficient framework combining experimental testing, FE simulation, and validated regression modeling to predict the performance of adhesively bonded composite patch repairs under varying thermal and mechanical conditions.
Abstract
This study investigates adhesively bonded composite patch repair to enhance the load-carrying capacity of damaged metallic structures, introducing a novel FE-augmented machine learning (ML) framework that addresses the limited availability of experimental data in structural repair applications. To evaluate this approach, aluminum and steel specimens with central fatigue cracks were repaired using glass-fiber/epoxy and carbon-fiber/epoxy composite patches and tested under quasi-static loading at room (70 F °), high (145 F °), and low (−60 F °) temperatures. Finite element (FE) models were then developed in ABAQUS© to predict the failure loads of the patched specimens under varying temperature conditions, showing excellent agreement with the experimental data. The high accuracy of the FE predictions enabled their use as additional training data, effectively augmenting the limited experimental dataset and allowing the development of more robust regression models. Ten machine learning (ML) regression models, including linear regression (LR), polynomial regression (PR), support vector regression (SVR), random forest (RF), gradient boosting (GB), XGBoost (XGB), LightGBM (LGBM), Gaussian process (GP) regression, artificial neural networks (ANNs), and Kolmogorov–Arnold networks (KANs), were trained to predict the failure load of both unpatched and patched specimens as a function of material type, temperature, specimen thickness, crack length, and, for patched specimens, patch type and thickness. The datasets combined a limited set of physical results (75 patched samples: 63 experimental and 12 finite-element; 72 unpatched samples: 27 experimental and 45 theoretical) with Gaussian-mixture-model synthetic samples used only to augment the training data up to 300 samples per case. Under a configuration-grouped, leakage-free nested cross-validation (entire configurations held out for testing, hyperparameters tuned on inner folds only), the best models predicted the failure load of unseen configurations with mean absolute percentage errors of 2.78% (Gradient Boosting, patched, R2=0.87) and 3.33% (Gaussian Process, unpatched, R2=0.98). A paired ablation showed that Gaussian-mixture-model augmentation did not improve accuracy and, for several models, actually reduced it; the final models therefore rely on the real multi-source (experimental, FE, and theoretical) data, with the synthetic pipeline reported as a validated but non-beneficial component for these datasets. Overall, this study provides a novel, data-efficient framework combining experimental testing, FE simulation, and validated regression modeling to predict the performance of adhesively bonded composite patch repairs under varying thermal and mechanical conditions.