Prediction of ultimate strength and strain in FRP wrapped oval shaped concrete columns using machine learning
Li Shang, Haytham F. Isleem, Walaa J. K. Almoghayer, Mohammad Khishe
Abstract
The accurate prediction of the strength enhancement ratio ( $$f_{{cc}} /f_{c} ^{\prime}$$ ) and strain enhancement ratio (εcc/εco) in FRP-wrapped elliptical concrete columns is crucial for optimizing structural performance. This study employs machine learning (ML) techniques to enhance prediction accuracy and reliability. A dataset of 181 samples, derived from experimental studies and finite element modeling, was utilized, with a 70:30 train-test split (127 training samples and 54 testing samples). Four ML models: Decision Tree (DT), Adaptive Boosting (ADB), Stochastic Gradient Boosting (SGB), and Extreme Gradient Boosting (XGB) were trained and optimized using Bayesian Optimization to refine their hyperparameters and improve performance.Results demonstrate that SGB achieved the best performance for predicting $$f_{{cc}} /f_{c} ^{\prime}$$ , with an R2 of 0.850, the lowest RMSE (0.190), and the highest generalization capability, making it the most reliable model for strength enhancement predictions. For strain enhancement prediction (εcc/εco), XGB outperformed other models, achieving an R2 of 0.779 with the lowest RMSE (2.162), indicating a better balance between accuracy, generalization, and minimal overfitting. DT and ADB exhibited lower predictive performance, with higher residual errors and lower generalization capacity. Furthermore, Shapley Additive exPlanations analysis identified the FRP thickness-elastic modulus product (tf × Ef) and concrete compressive strength ( $$f_{c} ^{\prime}$$ ) as the most influential features impacting both enhancement ratios. To facilitate real-world applications, an interactive graphical user interface was developed, enabling engineers to input ten structural parameters and obtain real-time predictions.