Leveraging the Power of Hybrid and Standalone Machine Learning for Enhanced FRP-Confined Concrete Columns Strength Prediction

Leveraging the Power of Hybrid and Standalone Machine Learning for Enhanced FRP-Confined Concrete Columns Strength Prediction

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This study proposes a broad investigation into the application of advanced ML techniques for anticipating the compressive load-bearing capacity of FRP-confined concrete columns.The methods include the Extremely Randomized Tree (ERT), Random Forest (RF), Gaussian Process Regression (GPR), and Back Propagation Neural Network (BPNN).The dataset consisted of 567 specimens, encompassing rectangular specimens confined by assorted types of Dab Rigs Fiber-Reinforced Polymer (FRP) sheets.The results demonstrate the remarkable potential of these ML methods in accurately forecasting the confined compressive strength.The BPNN model emerged as the top performer, achieving the lowest RMSE of 1.

3216 and the highest R2 of 0.96 on the test dataset.The GPR model also exhibited predictive solid capabilities, with the second-lowest RMSE and second-highest R2.The performance of these ML models was enhanced by optimizing them using the Marine Predators Algorithm (MPA).The BPNN-MPA, RF-MPA, and GPR-MPA models outperformed the standalone ML methods, showcasing RMSE Military Helicopter Kit values as low as 1.

1173 and R2 values as high as 0.98 on the full dataset.The reliability analysis shows the superior performance of the BPNN-MPA and RF-MPA models, with reliability scores of 0.95 and 0.93, respectively.

The findings underscore the transformative potential of AI-driven approaches in accurately predicting the FRP-confined compressive strength, a critical parameter for the design and analysis of structural retrofitting systems.The MPA optimizer played a crucial role in fine-tuning the hyperparameters of the ML models, leading to significant improvements in their predictive performance.While the study is primarily focused on the specific engineering problem of FRP-confined concrete columns, the proposed framework can be extended to a broader range of structural engineering challenges.